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Following confirmation of the first case of the ongoing U.S. HPAI H5N1 epizootic in commercial poultry on February 8, 2022, the virus has continued to devastate the U.S. poultry sector and the pathogen has since managed to cross over to livestock and a few human cases have also been reported. Efficient outbreak management benefits greatly from timely detection and proper identification of the pathways of virus introduction and spread. In this study, we used changes in mortality rates as a proxy for HPAI incidence in a layer, broiler and turkey flock together with diagnostic test results to infer within-flock HPAI transmission dynamics. Mathematical modeling techniques, specifically the Approximate Bayesian Computation algorithm in conjunction with a stochastic within-flock HPAI transmission model were used in the analysis. The time window of HPAI virus introduction into the flock (TOI) and the adequate contact rate (ACR) were estimated. Then, using the estimated TOI together with the day when the first HPAI positive sample was collected from the flock, we calculated the most likely time to first positive sample (MTFPS) which reflects the time to HPAI detection. The estimated joint (i.e., all species combined) median of the MTFPS for different flocks was six days, the joint median most likely ACR was 6.8 newly infected birds per infectious bird per day, the joint median R 0 was 13 and the joint median number of test days per flock was two. These results were also grouped by species and by epidemic phase and discussed accordingly. We conclude that this findings from this and other related studies are beneficial for the different stakeholders in outbreak management. We recommend that combining TOI analysis with complementary approaches such as phylogenetic analyses is critically important for improved understanding of disease transmission pathways. The estimated parameters can also be used to parametrize mathematical models that can guide the design of surveillance protocols, risk analyses of HPAI spread, and emergency preparedness for HPAI outbreaks.

The necessary response to a livestock transboundary infectious disease outbreak will likely outpace available resources. Consequently, policymakers need strategies to inform decisions about allocating limited resources. This study aimed to develop a stochastic framework that strategically examines vaccine allocation in a series of simultaneous multi-player decision sets, using game theory and allocation rules. We modeled 11 stochastic foot-and-mouth disease (FMD) scenarios using InterSpread Plus (Version 6.01.44). Stakeholders were designated as decision-maker one (DM1, the index state) and decision-maker two (DM2, a group of three neighboring states) requesting all or a share of the available vaccines. We selected two outcome criteria for examination: outbreak size and duration. Vaccine allocation strategies were determined by four rules. Rule 1 prioritized allocation to the index state, rule 2 prioritized allocation to neighboring states, rule 3 provided equal prioritization, and rule 4 prioritized allocation based on the percentage of dairy cattle in each state. For each scenario, 300 iterations were completed using matched random seeds. The outcome rankings of each matched iteration were treated as the payoffs of DMs and were analyzed as static games with perfect information. Nash equilibrium and Pareto optimal solutions were summarized across scenarios and iterations within each rule. Decision positions were evaluated per allocation rule according to game theory equilibrium principles. Rule 3, equal prioritization, resulted in a Pareto optimal solution more frequently, benefiting both DMs, and had the highest level of agreement in decision-states between Nash equilibrium and Pareto optimal outcomes. For rules 1-3, Pareto optimal solutions did not consistently result in lower outbreak size and duration (90th percentile) for both DMs when compared to Nash equilibrium solutions. Under rule 4, outbreak size and duration metrics showed less differentiation between decision positions. This stochastic framework incorporates epidemiological data and accounts for the payoffs resulting from multiple stakeholders' choices. This approach can aid in decision-making for scarce resource allocation in contexts where individual payoffs depend on others' choices. Additionally, these findings contribute to improving preparedness for an outbreak of FMD in disease-free regions.

In March 2017, two commercial broiler breeder operations were confirmed with H7N9 highly pathogenic avian influenza (HPAI), and an additional six commercial broiler breeder operations were found positive with an H7N9 low pathogenicity avian influenza virus (LPAIV) or an H7 LPAIV (N type not identified). To better understand conditions leading up to testing positive for AI, egg production and mortality data for the 6 mo before the outbreak were obtained from five case farms (two HPAIV-infected farms and three LPAIV-infected farms) and two control farms. Both HPAI farms experienced a sudden spike in mortality immediately before testing positive. Two LPAI farms experienced drops in egg production along with slight increases in mortality that occurred after a negative serologic test and before a positive PCR test. The third LPAI farm also had a notable drop in egg production with a coinciding increase in mortality before testing positive for AIV (last negative test date not available). Additionally, both HPAI farms and two LPAI farms reported mild respiratory illnesses in the weeks prior to testing positive for AI. Control farms did not experience similar drops in production or increase in mortality. Clinical signs on LPAI farms were mild and easily confused with background health patterns, suggesting the need for improved sensitivity to identify LPAI quickly. Applying a trigger of a 2% drop in egg production along with a mortality of 8 per 10 000 hens in individual barns showed that all case farms would be identified and uninfected farms would be falsely triggered on 1% of days monitored.

Following confirmation of the first case of the ongoing U.S. HPAI H5N1 epizootic in commercial poultry on February 8, 2022, the virus has continued to devastate the U.S. poultry sector and the pathogen has since managed to cross over to livestock and a few human cases have also been reported. Efficient outbreak management benefits greatly from timely detection and proper identification of the pathways of virus introduction and spread. In this study, using changes in mortality rates as a proxy for HPAI incidence in a layer, broiler and turkey flock, mathematical modeling techniques, specifically the Approximate Bayesian Computation algorithm in conjunction with a stochastic within-flock HPAI transmission model, were used to estimate the time window of pathogen introduction into the flock (TOI) and adequate contact rate (ACR) based on the daily mortality and diagnostic test results. The estimated TOI was then used together with the day when the first positive sample was collected to calculate the most likely time to first positive sample (MTFPS) which reflects the time to HPAI detection in the flock. The estimated joint (i.e., all species combined) median of the MTFPS for different flocks was six days, the joint median most likely ACR was 6.8 newly infected birds per infectious bird per day, the joint median R0was 13 and the joint median number of test days per flock was two. These results were also grouped by species and by epidemic phase and discussed accordingly. We conclude that findings from this and related studies are beneficial for the different stakeholders in outbreak management and combining TOI analysis with complementary approaches such as phylogenetic analyses is critically important for improved understanding of disease transmission pathways. The estimated parameters can also inform models used for surveillance design, risk analysis, and emergency preparedness.

Emergency response exercises have recognized issues associated with traditional control of foot-and-mouth disease (FMD) in large feedlots. The depopulation and disposal of large numbers of animals poses difficult challenges for environmental management and resource requirements. The study has focused on the epidemiological and economic consequences of allowing infected cattle to recover at the feedlot, as well as the economic consequences including uncertainty in trade sanctions.

Learning to predict reward is thought to be driven by dopaminergic prediction errors, which reflect discrepancies between actual and expected value. Here the authors show that learning to predict neutral events is also driven by prediction errors and that such value-neutral associative learning is also likely mediated by dopaminergic error signals. Associative learning is driven by prediction errors. Dopamine transients correlate with these errors, which current interpretations limit to endowing cues with a scalar quantity reflecting the value of future rewards. We tested whether dopamine might act more broadly to support learning of an associative model of the environment. Using sensory preconditioning, we show that prediction errors underlying stimulus–stimulus learning can be blocked behaviorally and reinstated by optogenetically activating dopamine neurons. We further show that suppressing the firing of these neurons across the transition prevents normal stimulus–stimulus learning. These results establish that the acquisition of model-based information about transitions between nonrewarding events is also driven by prediction errors and that, contrary to existing canon, dopamine transients are both sufficient and necessary to support this type of learning. Our findings open new possibilities for how these biological signals might support associative learning in the mammalian brain in these and other contexts.

Dopamine neurons are proposed to signal the reward prediction error in model-free reinforcement learning algorithms. This term represents the unpredicted or ‘excess’ value of the rewarding event, value that is then added to the intrinsic value of any antecedent cues, contexts or events. To support this proposal, proponents cite evidence that artificially-induced dopamine transients cause lasting changes in behavior. Yet these studies do not generally assess learning under conditions where an endogenous prediction error would occur. Here, to address this, we conducted three experiments where we optogenetically activated dopamine neurons while rats were learning associative relationships, both with and without reward. In each experiment, the antecedent cues failed to acquire value and instead entered into associations with the later events, whether valueless cues or valued rewards. These results show that in learning situations appropriate for the appearance of a prediction error, dopamine transients support associative, rather than model-free, learning. Dopamine neurons are proposed to signal the reward prediction error in model-free reinforcement learning algorithms. Here, the authors show that when given during an associative learning task, optogenetic activation of dopamine neurons causes associative, rather than value, learning.

The orbitofrontal cortex (OFC) is necessary for inferring value in tests of model-based reasoning, including in sensory preconditioning. This involvement could be accounted for by representation of value or by representation of broader associative structure. We recently reported neural correlates of such broader associative structure in OFC during the initial phase of sensory preconditioning (Sadacca et al., 2018). Here, we used optogenetic inhibition of OFC to test whether these correlates might be necessary for value inference during later probe testing. We found that inhibition of OFC during cue-cue learning abolished value inference during the probe test, inference subsequently shown in control rats to be sensitive to devaluation of the expected reward. These results demonstrate that OFC must be online during cue-cue learning, consistent with the argument that the correlates previously observed are not simply downstream readouts of sensory processing and instead contribute to building the associative model supporting later behavior.

Sensory preconditioning has been used to implicate midbrain dopamine in model-based learning, contradicting the view that dopamine transients reflect model-free value. However, it has been suggested that model-free value might accrue directly to the preconditioned cue through mediated learning. Here, building on previous work (Sadacca et al., 2016), we address this question by testing whether a preconditioned cue will support conditioned reinforcement in rats. We found that while both directly conditioned and second-order conditioned cues supported robust conditioned reinforcement, a preconditioned cue did not. These data show that the preconditioned cue in our procedure does not directly accrue model-free value and further suggest that the cue may not necessarily access value even indirectly in a model-based manner. If so, then phasic response of dopamine neurons to cues in this setting cannot be described as signaling errors in predicting value.