Dynamics of the COVID-19 epidemic in Ireland under mitigation.

BACKGROUND: In Ireland and across the European Union the COVID-19 epidemic waves, driven mainly by the emergence of new variants of the SARS-CoV-2 have continued their course, despite various interventions from governments. Public health interventions continue in their attempts to control the spread as they wait for the planned significant effect of vaccination. METHODS: To tackle this challenge and the observed non-stationary aspect of the epidemic we used a modified SEIR stochastic model with time-varying parameters, following Brownian process. This enabled us to reconstruct the temporal evolution of the transmission rate of COVID-19 with the non-specific hypothesis that it follows a basic stochastic process constrained by the available data. This model is coupled with Bayesian inference (particle Markov Chain Monte Carlo method) for parameter estimation and utilized mainly well-documented Irish hospital data. RESULTS: In Ireland, mitigation measures provided a 78-86% reduction in transmission during the first wave between March and May 2020. For the second wave in October 2020, our reduction estimation was around 20% while it was 70% for the third wave in January 2021. This third wave was partly due to the UK variant appearing in Ireland. In June 2020 we estimated that sero-prevalence was 2.0% (95% CI: 1.2-3.5%) in complete accordance with a sero-prevalence survey. By the end of April 2021, the sero-prevalence was greater than 17% due in part to the vaccination campaign. Finally we demonstrate that the available observed confirmed cases are not reliable for analysis owing to the fact that their reporting rate has as expected greatly evolved. CONCLUSION: We provide the first estimations of the dynamics of the COVID-19 epidemic in Ireland and its key parameters. We also quantify the effects of mitigation measures on the virus transmission during and after mitigation for the three waves. Our results demonstrate that Ireland has significantly reduced transmission by employing mitigation measures, physical distancing and lockdown. This has to date avoided the saturation of healthcare infrastructures, flattened the epidemic curve and likely reduced mortality. However, as we await for a full roll out of a vaccination programme and as new variants potentially more transmissible and/or more infectious could continue to emerge and mitigation measures change silent transmission, challenges remain.

A mechanistic and data-driven reconstruction of the time-varying reproduction number: Application to the COVID-19 epidemic.

The effective reproduction number Reff is a critical epidemiological parameter that characterizes the transmissibility of a pathogen. However, this parameter is difficult to estimate in the presence of silent transmission and/or significant temporal variation in case reporting. This variation can occur due to the lack of timely or appropriate testing, public health interventions and/or changes in human behavior during an epidemic. This is exactly the situation we are confronted with during this COVID-19 pandemic. In this work, we propose to estimate Reff for the SARS-CoV-2 (the etiological agent of the COVID-19), based on a model of its propagation considering a time-varying transmission rate. This rate is modeled by a Brownian diffusion process embedded in a stochastic model. The model is then fitted by Bayesian inference (particle Markov Chain Monte Carlo method) using multiple well-documented hospital datasets from several regions in France and in Ireland. This mechanistic modeling framework enables us to reconstruct the temporal evolution of the transmission rate of the COVID-19 based only on the available data. Except for the specific model structure, it is non-specifically assumed that the transmission rate follows a basic stochastic process constrained by the observations. This approach allows us to follow both the course of the COVID-19 epidemic and the temporal evolution of its Reff(t). Besides, it allows to assess and to interpret the evolution of transmission with respect to the mitigation strategies implemented to control the epidemic waves in France and in Ireland. We can thus estimate a reduction of more than 80% for the first wave in all the studied regions but a smaller reduction for the second wave when the epidemic was less active, around 45% in France but just 20% in Ireland. For the third wave in Ireland the reduction was again significant (>70%).

Stochastic Epidemic Models inference and diagnosis with Poisson Random Measure Data Augmentation.

We present a new Bayesian inference method for compartmental models that takes into account the intrinsic stochasticity of the process. We show how to formulate a SIR-type Markov jump process as the solution of a stochastic differential equation with respect to a Poisson Random Measure (PRM), and how to simulate the process trajectory deterministically from a parameter value and a PRM realization. This forms the basis of our Data Augmented MCMC, which consists of augmenting parameter space with the unobserved PRM value. The resulting simple Metropolis-Hastings sampler acts as an efficient simulation-based inference method, that can easily be transferred from model to model. Compared with a recent Data Augmentation method based on Gibbs sampling of individual infection histories, PRM-augmented MCMC scales much better with epidemic size and is far more flexible. It is also found to be competitive with Particle MCMC for moderate epidemics when using approximate simulations. PRM-augmented MCMC also yields a posteriori estimates of the PRM, that represent process stochasticity, and which can be used to validate the model. A pattern of deviation from the PRM prior distribution will indicate that the model underfits the data and help to understand the cause. We illustrate this by fitting a non-seasonal model to some simulated seasonal case count data. Applied to the Zika epidemic of 2013 in French Polynesia, our approach shows that a simple SEIR model cannot correctly reproduce both the initial sharp increase in the number of cases as well as the final proportion of seropositive. PRM augmentation thus provides a coherent story for Stochastic Epidemic Model inference, where explicitly inferring process stochasticity helps with model validation.

Parallel trends in the transmission of SARS-CoV-2 and retail/recreation and public transport mobility during non-lockdown periods.

Recent literature strongly supports the hypothesis that mobility restriction and social distancing play a crucial role in limiting the transmission of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). During the first wave of the coronavirus disease 2019 (COVID-19) pandemic, it was shown that mobility restriction reduced transmission significantly. This study found that, in the period between the first two waves of the COVID-19 pandemic, there was high positive correlation between trends in the transmission of SARS-CoV-2 and mobility. These two trends oscillated simultaneously, and increased mobility following the relaxation of lockdown rules was significantly associated with increased transmission. From a public health perspective, these results highlight the importance of tracking changes in mobility when relaxing mitigation measures in order to anticipate future changes in the spread of SARS-CoV-2.

Poultry farmer response to disease outbreaks in smallholder farming systems in southern Vietnam.

Avian influenza outbreaks have been occurring on smallholder poultry farms in Asia for two decades. Farmer responses to these outbreaks can slow down or accelerate virus transmission. We used a longitudinal survey of 53 small-scale chicken farms in southern Vietnam to investigate the impact of outbreaks with disease-induced mortality on harvest rate, vaccination, and disinfection behaviors. We found that in small broiler flocks (≤16 birds/flock) the estimated probability of harvest was 56% higher when an outbreak occurred, and 214% higher if an outbreak with sudden deaths occurred in the same month. Vaccination and disinfection were strongly and positively correlated with the number of birds. Small-scale farmers - the overwhelming majority of poultry producers in low-income countries - tend to rely on rapid sale of birds to mitigate losses from diseases. As depopulated birds are sent to markets or trading networks, this reactive behavior has the potential to enhance onward transmission.

The past few decades have seen the circulation of avian influenza viruses increase in domesticated poultry, regularly creating outbreaks associated with heavy economic loss. In addition, these viruses can sometimes 'jump' into humans, potentially allowing new diseases ­ including pandemics ­ to emerge. The Mekong river delta, in southern Vietnam, is one of the regions with the highest circulation of avian influenza. There, a large number of farmers practice poultry farming on a small scale, with limited investments in disease prevention such as vaccination or disinfection. Yet, it was unclear how the emergence of an outbreak could change the behavior of farmers. To learn more, Delabouglise et al. monitored 53 poultry farms, with fewer than 1000 chickens per farm, monthly for over a year and a half. In particular, they tracked when outbreaks occurred on each farm, and how farmers reacted. Overall, poultry farms with more than 17 chickens were more likely to vaccinate their animals and use disinfection practices than smaller farms. However, disease outbreaks did not affect vaccination or disinfection practices. When an outbreak occurred, farmers with fewer than 17 chickens tended to sell their animals earlier. For instance, they were 214% more likely to send their animals to market if an outbreak with sudden deaths occurred that month. Even if they do not make as much money selling immature individuals, this strategy may allow them to mitigate economical loss: they can sell animals that may die soon, saving on feeding costs and potentially avoiding further contamination. However, as animals were often sold alive in markets or to itinerant sellers, this practice increases the risk of spreading diseases further along the trade circuits. These data could be most useful to regional animal health authorities, which have detailed knowledge of local farming systems and personal connections in the communities where they work. This can allow them to effect change. They could work with small poultry farmers to encourage them to adopt efficient disease management strategies. Ultimately, this could help control the spread of avian influenza viruses, and potentially help to avoid future pandemics.

Poultry population dynamics and mortality risks in smallholder farms of the Mekong river delta region.

BACKGROUND: Poultry farming is widely practiced by rural households in Vietnam and the vast majority of domestic birds are kept on small household farms. However, smallholder poultry production is constrained by several issues such as infectious diseases, including avian influenza viruses whose circulation remains a threat to public health. This observational study describes the demographic structure and dynamics of small-scale poultry farms of the Mekong river delta region. METHOD: Fifty three farms were monitored over a 20-month period, with farm sizes, species, age, arrival/departure of poultry, and farm management practices recorded monthly. RESULTS: Median flock population sizes were 16 for chickens (IQR: 10-40), 32 for ducks (IQR: 18-101) and 11 for Muscovy ducks (IQR: 7-18); farm size distributions for the three species were heavily right-skewed. Muscovy ducks were kept for long periods and outdoors, while chickens and ducks were farmed indoors or in pens. Ducks had a markedly higher removal rate (broilers: 0.14/week; layer/breeders: 0.05/week) than chickens and Muscovy ducks (broilers: 0.07/week; layer/breeders: 0.01-0.02/week) and a higher degree of specialization resulting in a substantially shorter life span. The rate of mortality due to disease did not differ much among species, with birds being less likely to die from disease at older ages, but frequency of disease symptoms differed by species. Time series of disease-associated mortality were correlated with population size for Muscovy ducks (Kendall's coefficient τ = 0.49, p-value < 0.01) and with frequency of outdoor grazing for ducks (τ = 0.33, p-value = 0.05). CONCLUSION: The study highlights some challenges to disease control in small-scale multispecies poultry farms. The rate of interspecific contact and overlap between flocks of different ages is high, making small-scale farms a suitable environment for pathogens circulation. Muscovy ducks are farmed outdoors with little investment in biosecurity and few inter-farm movements. Ducks and chickens are more at-risk of introduction of pathogens through movements of birds from one farm to another. Ducks are farmed in large flocks with high turnover and, as a result, are more vulnerable to disease spread and require a higher vaccination coverage to maintain herd immunity.

No Evidence of On-farm Circulation of Avian Influenza H5 Subtype in Ca Mau Province, Southern Vietnam, March 2016 - January 2017.

BACKGROUND: Subtype H5N1 avian influenza viruses, both high pathogenicity and low pathogenicity, have been enzootic in Vietnam since 2001.  The viruses are readily identified at live bird markets, but virus prevalence on smallholder poultry is typically zero or very low.  If the true direction of the viral transmission chain is farm to market, it is unknown why farm prevalence should be low when market prevalence is moderate to high. METHODS: We established a cohort of 50 smallholder poultry farms in Ca Mau province in the Mekong Delta region of Vietnam.  From March 2016 to January 2017, we collected naso-pharyngeal and cloacal samples from 156 ducks and 96 chickens.  In addition, 126 environmental samples were collected.  Samples were assayed for H5 subtype influenza by real-time RT-PCR. Results/Discussion: None of the 378 collected samples were positive for H5 influenza.  This is likely to mean that circulation of subtype H5 influenza viruses was low in Ca Mau in 2016.  Detection of avian influenza on smallholder poultry farms is necessary to determine the directionality and association between farm prevalence and market prevalence of avian influenza viruses.  Larger farm-level studies should be planned as these will be critical for determining the presence and strength of this association.