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Optimising predictive models to prioritise viral discovery in zoonotic reservoirs.

ABSTRACT: Despite the global investment in One Health disease surveillance, it remains difficult and costly to identify and monitor the wildlife reservoirs of novel zoonotic viruses. Statistical models can guide sampling target prioritisation, but the predictions from any given model might be highly uncertain; moreover, systematic model validation is rare, and the drivers of model performance are consequently under-documented. Here, we use the bat hosts of betacoronaviruses as a case study for the data-driven process of comparing and validating predictive models of probable reservoir hosts. In early 2020, we generated an ensemble of eight statistical models that predicted host-virus associations and developed priority sampling recommendations for potential bat reservoirs of betacoronaviruses and bridge hosts for SARS-CoV-2. During a time frame of more than a year, we tracked the discovery of 47 new bat hosts of betacoronaviruses, validated the initial predictions, and dynamically updated our analytical pipeline. We found that ecological trait-based models performed well at predicting these novel hosts, whereas network methods consistently performed approximately as well or worse than expected at random. These findings illustrate the importance of ensemble modelling as a buffer against mixed-model quality and highlight the value of including host ecology in predictive models. Our revised models showed an improved performance compared with the initial ensemble, and predicted more than 400 bat species globally that could be undetected betacoronavirus hosts. We show, through systematic validation, that machine learning models can help to optimise wildlife sampling for undiscovered viruses and illustrates how such approaches are best implemented through a dynamic process of prediction, data collection, validation, and updating.

SUBMITTER: Becker DJ

PROVIDER: S-EPMC8747432 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND:Bartonella spp. are vector-borne pathogens transmitted to humans via blood-sucking arthropods. Rodents such as the black rat (Rattus rattus) and Norway rat (R. norvegicus) are thought to be the main reservoirs. An infection with rodent-associated Bartonella spp. may cause severe symptoms in humans such as endocarditis and neuroretinitis. The current knowledge of Bartonella prevalence in rats from western Europe is scarce. METHODS:Rats and a few other rodent by-catches were trapped in the context of a rodenticide resistance study at different sites in Flanders, Belgium. During dissection, biometric data were collected, and spleen tissues were taken. DNA was extracted from spleen samples and tested for Bartonella spp. by conventional generic polymerase chain reaction (PCR). To determine the Bartonella species, a selected number of amplicons were sequenced and compared with GenBank entries. RESULTS:In total, 1123 rodents were trapped. The predominate species was R. norvegicus (99.64%). Other rodents trapped included: two water voles (Arvicola amphibius, 0.18%); one colour rat (R. norvegicus forma domestica, 0.09%); and one muskrat (Ondatra zibethicus, 0.09%). PCR analysis of 1097 rodents resulted in 410 (37.37%, 95% CI: 34.50-40.31%) Bartonella spp. DNA-positive samples. Bartonella tribocorum (94.68%, 95% CI: 88.02-98.25%) was the most frequently detected Bartonella species, followed by B. grahamii (3.19%, 95% CI: 0.66-9.04%) and B. doshiae (1.06%, 95% CI: 0.03-5.79%). An uncultured Bartonella species occurred in one water vole (1.06%, 95% CI: 0.03-5.79%). There was a significantly higher Bartonella prevalence in older rats compared to juveniles and a significant difference in Bartonella prevalence concerning the localisation of trapping sites. In contrast, there was no statistically significant difference in Bartonella prevalence regarding sex, degree of urbanisation and season. CONCLUSIONS:Based on the high prevalence found, we conclude that the Norway rat seems to be a key reservoir host for zoonotic B. tribocorum in Belgium.

Project description:BackgroundStatistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.Methodology/principal findingsWe developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.Conclusions/significanceWe demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.

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Optimising predictive models to prioritise viral discovery in zoonotic reservoirs.

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