When to vaccinate a fluctuating wildlife population: Is timing everything?

Schreiner, Courtney; Nuismer, Scott L.; Basinski, Andrew

doi:10.1111/1365-2664.13539

Cited by 14 publications

(18 citation statements)

References 42 publications

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“…At least in Guinea and Sierra Leone, research on the population dynamics of M. natalensis indicates that density fluctuations are much weaker than those in East Africa [ 83 ]. In the case of rodent vaccination, understanding population dynamics is particularly important because distributing vaccines at seasonal population lows in wildlife demographic cycles can, in theory, substantially increase the probability of pathogen elimination [ 83 , 84 ].…”

Section: Discussionmentioning

confidence: 99%

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

et al. 2021

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Forecasting the risk of pathogen spillover from reservoir populations of wild or domestic animals is essential for the effective deployment of interventions such as wildlife vaccination or culling. Due to the sporadic nature of spillover events and limited availability of data, developing and validating robust, spatially explicit, predictions is challenging. Recent efforts have begun to make progress in this direction by capitalizing on machine learning methodologies. An important weakness of existing approaches, however, is that they generally rely on combining human and reservoir infection data during the training process and thus conflate risk attributable to the prevalence of the pathogen in the reservoir population with the risk attributed to the realized rate of spillover into the human population. Because effective planning of interventions requires that these components of risk be disentangled, we developed a multi-layer machine learning framework that separates these processes. Our approach begins by training models to predict the geographic range of the primary reservoir and the subset of this range in which the pathogen occurs. The spillover risk predicted by the product of these reservoir specific models is then fit to data on realized patterns of historical spillover into the human population. The result is a geographically specific spillover risk forecast that can be easily decomposed and used to guide effective intervention. Applying our method to Lassa virus, a zoonotic pathogen that regularly spills over into the human population across West Africa, results in a model that explains a modest but statistically significant portion of geographic variation in historical patterns of spillover. When combined with a mechanistic mathematical model of infection dynamics, our spillover risk model predicts that 897,700 humans are infected by Lassa virus each year across West Africa, with Nigeria accounting for more than half of these human infections.

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Section: Discussionmentioning

confidence: 99%

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

et al. 2021

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“…The demographic model includes a periodic Gaussian birth rate: b ( t ) = k exp[− σ cos 2 ( πt /365)]. Other forms of periodicity such as a sinusoidal forcing function or step function could also be used to impose seasonality [25, 26], but Peel et al [27] found broad support for a periodic Gaussian birth rate in 17 out of 18 datasets on the timing of births in wild animals. The parameter k was chosen to ensure an average population size ν :…”

Section: Methodsmentioning

confidence: 99%

Reservoir population dynamics and pathogen epidemiology drive pathogen genetic diversity, spillover, and emergence

Remien

Nuismer

2020

Preprint

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When several factors align, pathogens that normally infect wildlife can spill over into the human population. If pathogen transmission within the human population is self-sustaining, or rapidly evolves to be self sustaining, novel human pathogens can emerge. Although many factors influence the likelihood of spillover and emergence, the rate of contact between humans and wildlife is critical. Thus, for those pathogens inhabiting wildlife reservoirs with pronounced seasonal fluctuations in population density, it is broadly recognized that spillover risk also varies with season. What remains unknown, however, is the extent to which seasonal fluctuations in reservoir populations influence the evolutionary dynamics of pathogens in ways that affect the likelihood of emergence. Here, we use mathematical models and stochastic simulations to show that seasonal fluctuations in reservoir population densities lead to seasonal increases in genetic variation within pathogen populations and thus influence the waiting time for mutations capable of sustained human-to-human transmission. These seasonal increases in genetic variation also lead to elevated risk of emergence at predictable times of year.

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“…Temporal fluctuations in population density, due to seasonal rainfall, would provide another important insight into the seasonal burden of human LF cases [11]. Understanding this ecological connection is important because distributing vaccines at seasonal population lows in wildlife demographic cycles can, in theory, substantially increase the probability of pathogen elimination [58, 59]. Incorporating these temporal layers will become more feasible as more time-series data on population density in the focal reservoir species become available.…”

Section: Discussionmentioning

confidence: 99%

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

Basinski

Fichet‐Calvet

Sjödin

et al. 2020

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27Forecasting how the risk of pathogen spillover changes over space is essential 28 for the effective deployment of interventions such as human or wildlife vacci-29 nation. However, due to the sporadic nature of spillover events, developing 30 robust predictions is challenging. Recent efforts to overcome this obstacle have 31 capitalized on machine learning to predict spillover risk. A weakness of these 32 approaches has been their reliance on human infection data, which is known 33 to suffer from strongly biased reporting. We develop a novel approach that 34 combines sub-models for reservoir species distribution, pathogen distribution, 35 and transmission into the human population. We apply our method to Lassa 36 virus, a zoonotic pathogen with a high threat of emergence in West Africa. The 37 resulting model predicts the distribution of Lassa virus spillover risk and allows 38 us to revise existing estimates for the annual number of new human infections. 39 Our model predicts that between 961,300 -4,037,400 humans are infected by 40 Lassa virus each year, an estimate that exceeds current conventional wisdom. 41 Our model also predicts that Nigeria accounts for more than half of all new 42 Lassa cases in humans, making it a high-risk area for Lassa virus to become an 43 emergent pathogen. 44 2 Keywords 45 Lassa, Machine learning, zoonotic pathogen, emerging infectious disease, spillover, 46 risk map 47 48 Emerging infectious diseases (EIDs) pose a deadly threat to mankind. Approx-49 imately 40% of EIDs are caused by pathogens that circulate in a non-human 50 2 wildlife reservoir (i.e., zoonotic pathogens) [1]. Prior to full scale emergence, in-51 teraction between humans and wildlife creates opportunities for the occasional 52 transfer, or spillover, of the zoonotic pathogen into human populations [2]. 53 These initial spillover cases, in turn, can give an animal-borne pathogen a 54 foothold for genetic mutations that allow increased transmission among hu-55 mans [2, 3]. Consequently, a key step in preempting the threat of EIDs is 56 careful monitoring of when and where spillover into the human population is 57 occurring. However, because the majority of EIDs from wildlife originate in low 58 and middle income regions with limited health system infrastructure, accurately 59 estimating the rate and geographical range of pathogen spillover, and therefore 60 the risk of new EIDs, is a major challenge [1]. 61 Machine learning techniques have shown promise at predicting the geograph-62 ical range of spillover risk for several zoonotic diseases including Lassa fever [4-63 6], Ebola [7], and Leishmaniases [8]. Generally, these models are trained to 64 associate environmental features with the presence or absence of case reports 65 in humans or the associated reservoir. Once inferred from the training process, 66 the learned relationships between disease presence and the environment can be 67 extended across a region of interest. Using these techniques, previous studies of 68 Lassa fever (LF) ...

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When to vaccinate a fluctuating wildlife population: Is timing everything?

Cited by 14 publications

References 42 publications

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

Reservoir population dynamics and pathogen epidemiology drive pathogen genetic diversity, spillover, and emergence

Bridging the gap: Using reservoir ecology and human serosurveys to estimate Lassa virus spillover in West Africa

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