When and why direct transmission models can be used for environmentally persistent pathogens

Benson, Lee; Davidson, Ross S.; Green, Darren M.; Hoyle, Andrew; Hutchings, Michael R.; Marion, Glenn

doi:10.1371/journal.pcbi.1009652

Cited by 8 publications

(4 citation statements)

References 55 publications

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“…Both scenarios were able to explain observed dynamic patterns and yielded similar results in the context of interventions targeting chickens only. Previous theoretical work indeed demonstrated that both modes of transmission lead to similar dynamical outcomes, especially when environmental contamination unfolds on a fast time scale, and that it may be difficult to prefer one or another based solely on prevalence or incidence data 51 , 52 . This is a reassuring finding as it suggests that some epidemiological conclusions are not affected by precise modelling assumptions.…”

Section: Discussionmentioning

confidence: 99%

Modelling the transmission dynamics of H9N2 avian influenza viruses in a live bird market

Pinotti,

Kohnle,

Lourenço

et al. 2024

Nat Commun

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H9N2 avian influenza viruses (AIVs) are a major concern for the poultry sector and human health in countries where this subtype is endemic. By fitting a model simulating H9N2 AIV transmission to data from a field experiment, we characterise the epidemiology of the virus in a live bird market in Bangladesh. Many supplied birds arrive already exposed to H9N2 AIVs, resulting in many broiler chickens entering the market as infected, and many indigenous backyard chickens entering with pre-existing immunity. Most susceptible chickens become infected within one day spent at the market, owing to high levels of viral transmission within market and short latent periods, as brief as 5.3 hours. Although H9N2 AIV transmission can be substantially reduced under moderate levels of cleaning and disinfection, effective risk mitigation also requires a range of additional interventions targeting markets and other nodes along the poultry production and distribution network.

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Section: Discussionmentioning

confidence: 99%

Modelling the transmission dynamics of H9N2 avian influenza viruses in a live bird market

Pinotti,

Kohnle,

Lourenço

et al. 2024

Nat Commun

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“… CHAID is not able to handle zeros or out-of-order codes (for instance, it cannot be skipped from a code "3" to a code "6"). There's a chance that this will increase the amount of time spent recoding information 33 . …”

Section: Methodsmentioning

confidence: 99%

Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers

Onyema

Dalal

Obagbuwa

et al. 2022

Sci Rep

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Technology is playing an important role is healthcare particularly as it relates to disease prevention and detection. This is evident in the COVID-19 era as different technologies were deployed to test, detect and track patients and ensure COVID-19 protocol compliance. The White Spot Disease (WSD) is a very contagious disease caused by virus. It is widespread among shrimp farmers due to its mode of transmission and source. Considering the growing concern about the severity of the disease, this study provides a predictive model for diagnosis and detection of WSD among shrimp farmers using visualization and machine learning algorithms. The study made use of dataset from Mendeley repository. Machine learning algorithms; Random Forest classification and CHAID were applied for the study, while Python was used for implementation of algorithms and for visualization of results. The results achieved showed high prediction accuracy (98.28%) which is an indication of the suitability of the model for accurate prediction of the disease. The study would add to growing knowledge about use of technology to manage White Spot Disease among shrimp farmers and ensure real-time prediction during and post COVID-19.

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“…It is generally thought that pathogens experience a tradeoff between transmission rate (

β

) and virulence (

μ

) (Anderson & May, 1982; Cressler et al., 2016; Lipsitch & Moxon, 1997). These parameters are used in SIS models which are often used to study directly transmitted infectious diseases for which infection does not confer immunity (Hethcote, 2000) and can be used to describe environmentally transmitted diseases under some conditions (Benson et al., 2021). We define virulence as pathogen‐induced host mortality and note that although virulence is often taken to be a pathogen property, it is an emergent property of a host‐pathogen‐environment system (Turner et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Pathogen evolution following spillover from a resident to a migrant host population depends on interactions between host pace of life and tolerance to infection

Torstenson,

Shaw

2024

Journal of Animal Ecology

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Changes to migration routes and phenology create novel contact patterns among hosts and pathogens. These novel contact patterns can lead to pathogens spilling over between resident and migrant populations. Predicting the consequences of such pathogen spillover events requires understanding how pathogen evolution depends on host movement behaviour. Following spillover, pathogens may evolve changes in their transmission rate and virulence phenotypes because different strategies are favoured by resident and migrant host populations. There is conflict in current theoretical predictions about what those differences might be. Some theory predicts lower pathogen virulence and transmission rates in migrant populations because migrants have lower tolerance to infection. Other theoretical work predicts higher pathogen virulence and transmission rates in migrants because migrants have more contacts with susceptible hosts. We aim to understand how differences in tolerance to infection and host pace of life act together to determine the direction of pathogen evolution following pathogen spillover from a resident to a migrant population. We constructed a spatially implicit model in which we investigate how pathogen strategy changes following the addition of a migrant population. We investigate how differences in tolerance to infection and pace of life between residents and migrants determine the effect of spillover on pathogen evolution and host population size. When the paces of life of the migrant and resident hosts are equal, larger costs of infection in the migrants lead to lower pathogen transmission rate and virulence following spillover. When the tolerance to infection in migrant and resident populations is equal, faster migrant paces of life lead to increased transmission rate and virulence following spillover. However, the opposite can also occur: when the migrant population has lower tolerance to infection, faster migrant paces of life can lead to decreases in transmission rate and virulence. Predicting the outcomes of pathogen spillover requires accounting for both differences in tolerance to infection and pace of life between populations. It is also important to consider how movement patterns of populations affect host contact opportunities for pathogens. These results have implications for wildlife conservation, agriculture and human health.

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When and why direct transmission models can be used for environmentally persistent pathogens

Cited by 8 publications

References 55 publications

Modelling the transmission dynamics of H9N2 avian influenza viruses in a live bird market

Modelling the transmission dynamics of H9N2 avian influenza viruses in a live bird market

Bootstrapping random forest and CHAID for prediction of white spot disease among shrimp farmers

Pathogen evolution following spillover from a resident to a migrant host population depends on interactions between host pace of life and tolerance to infection

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