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Showing posts with the label mathematical models

#Risk #evaluation of newly emerging #flu viruses based on genomic #sequences and AI

Abstract The recent resurgence of highly pathogenic avian influenza H5N1 viruses in North America and Europe has heightened global concerns regarding potential influenza pandemics. Despite significant progress in the surveillance and prevention of emerging influenza viruses, effective tools for rapid and accurate risk assessment remain limited. Here, we present FluRisk , an innovative computational framework that integrates viral genomic data with artificial intelligence (AI) to enable rapid and comprehensive risk evaluation of emerging influenza strains. FluRisk incorporates a curated database of over 1,000 experimentally validated molecular markers linked to key viral phenotypes , including mammalian adaptation, mammalian virulence, mammalian transmission, human receptor-binding preference , and antiviral drug resistance . Leveraging these markers, we developed three state-of-the-art machine learning models to predict human adaptation, mammalian virulence, and human receptor-binding ...

Comparative #risk #assessment of spread of highly pathogenic avian #influenza #H5 viruses in French #broiler and layer sectors.

Abstract Since 2015, French poultry production is threatened almost every year by a reintroduction of highly pathogenic avian influenza H5 viruses . The duck sector was the most concerned by this crisis but other sectors such as broiler, layer and turkey were also affected by outbreaks. The objective of this work was to assess the risk of highly pathogenic avian influenza H5 virus transmission from one farm to another within the French broiler and layer production network. This study used the WOAH risk assessment framework . After drawing up a scenario tree of virus transmission from one farm to another, data were collected through a literature review or through experts' elicitation. Three questionnaires were developed according to the experts' field of expertise: avian influenza, broiler and layer sectors. The experts' estimates were combined using a beta distribution weighted by their confidence level. A Monte Carlo iteration process was used to combine the different prob...

Global #risk #mapping of highly pathogenic avian #influenza #H5N1 and H5Nx in the light of epidemic episodes occurring from 2020 onward

Abstract Avian influenza (AI) is a highly contagious viral disease affecting poultry and wild water birds , posing significant global challenges due to its high mortality rates and economic impacts. Highly pathogenic avian influenza (HPAI) outbreaks , particularly those caused by H5N1 and its variants , have surged since their first occurrence in 1959. The HPAI H5N1 clade 2.3.4.4b viruses have notably expanded its geographical reach, affecting numerous countries, diverse avian species, and now wild and domestic mammals . Using an ecological niche modelling approach, this study aims to elucidate the environmental factors associated with the increased HPAI H5 cases since 2020, investigate potential shifts in ecological niches , and predict new areas suitable for local viral circulation. Focusing on H5N1 and H5Nx strains, we have developed ecological niche models for HPAI case in both wild and domestic birds while considering two distinct periods: 2015-2020 and 2020-2022. Key environmenta...

Modeling Effects of Routine #Screening for Accidental #Lab-Acquired #Infections on #Risk of Potential #Pandemic Pathogen #Escape from High-Biosafety Research Facilities

Abstract Accidental lab–acquired infections (LAIs) risk releasing potential pandemic pathogens (PPPs) from BSL–3/4 facilities . We constructed a stochastic network infectious disease model to simulate how the probability of an outbreak of a pathogen resembling wild–type SARS–COV–2, following an initial LAI would be influenced by test–and–isolate interventions over a 100–day horizon . We varied test frequency (0–7 tests/week), peak sensitivity (50–100%), and isolation delay (0–3 days). For each of 192 parameter combinations, we conducted 1,000 simulations and used logistic regression to quantify how each parameter influenced the likelihood of an outbreak of 50 or more infections. Results indicated that even relatively infrequent routine testing significantly reduced the risk of outbreaks under diverse plausible scenarios, with greater reductions achieved at higher test frequencies. Once-weekly testing reduced outbreak risk by 52% under optimistic assumptions (80% sensitivity, 1–day dela...

Introducing a #framework for within-host #dynamics and #mutations modelling of #H5N1 #influenza infection in #humans

Abstract Avian influenza A(H5N1) poses a public health risk due to its pandemic potential should the virus mutate to become human-to-human transmissible. To date, reported influenza A(H5N1) human cases have typically occurred in the lower respiratory tract with a high case fatality rate . There is prior evidence of some influenza A(H5N1) strains being a small number of amino acid mutations away from achieving droplet transmissibility , possibly allowing them to be spread between humans. We present a mechanistic within-host influenza A(H5N1) infection model, novel for its explicit consideration of the biological differences between the upper and lower respiratory tracts. We then estimate a distribution of viral lifespans and effective replication rates in human H5N1 influenza cases. By combining our within-host model with a viral mutation model, we determine the probability of an infected individual generating a droplet transmissible strain of influenza A(H5N1) through mutation. For thr...

Modeling viral #shedding and #symptom #outcomes in #oseltamivir-treated experimental #influenza infection

Abstract Influenza remains a global public health concern, and although the antiviral drug oseltamivir is widely used to treat infections , questions regarding its actual antiviral efficacy and clinical benefits remain. Here, we evaluated the effects of oseltamivir on viral shedding dynamics in the context of experimental influenza infection . We analyzed individual participant data, including viral load, time to symptom alleviation, and laboratory test measurements, obtained from three publicly available clinical trials involving experimental infections with influenza A and B viruses. We applied mathematical modeling and estimated parameters using a nonlinear mixed-effects model to capture viral infection dynamics. Our analysis revealed that, compared with placebo groups, the oseltamivir-treated groups tended to have lower values in terms of viral load area under the curve , duration of infection, peak viral titer, and time to peak; however, most of these differences were not signific...

#Ecology and #environment predict spatially stratified #risk of #H5 highly pathogenic avian #influenza clade 2.3.4.4b in wild #birds across #Europe

Abstract Highly pathogenic avian influenza (HPAI) represents a threat to animal and human health , with the ongoing H5N1 outbreak within the H5 2.3.4.4b clade being the largest on record. However, it remains unclear what factors have contributed to its intercontinental spread . We use Bayesian additive regression trees, a machine learning method designed for probabilistic modelling of complex nonlinear phenomena, to construct species distribution models (SDMs) for HPAI clade 2.3.4.4b presence. We identify factors driving geospatial patterns of infection and project risk distributions across Europe . Our models are time-stratified to capture both seasonal changes in risk and shifts in epidemiology associated with the succession of H5N6/H5N8 by H5N1 within the clade . While previous studies aimed to model HPAI presence from physical geography, we explicitly consider wild bird ecology by including estimates of bird species richness, abundance of specific taxa, and "abundance indices...

Modelling a potential #zoonotic #spillover event of #H5N1 #influenza

Abstract Highly Pathogenic Avian Influenza (HPAI) is a prominent candidate for a future human pandemic arising from a zoonotic spillover event . Its best-known strain is H5N1 , with South- or South-East Asia a likely location for an initial outbreak. Such an outbreak would be initiated through a primary event of bird-to-human infection, followed by sustained human-to-human transmission . Early interventions would require the extraction, integration and interpretation of epidemiological information from the limited and noisy case data available at outbreak onset. We studied the implications of a potential zoonotic spillover of H5N1 influenza into humans. Our simulations used BharatSim , an agent-based model framework designed primarily for the population of India , but which can be tuned easily for others. We considered a synthetic population representing farm-workers (primary contacts) in a farm with infected birds. These primary contacts transfer infections to secondary (household) co...