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.

Transmissible vaccines have the potential to revolutionize infectious disease control by reducing the vaccination effort required to protect a population against a disease. Recent efforts to develop transmissible vaccines focus on recombinant transmissible vaccine designs (RTVs) because they pose reduced risk if intra-host evolution causes the vaccine to revert to its vector form. However, the shared antigenicity of the vaccine and vector may confer vaccine-immunity to hosts infected with the vector, thwarting the ability of the vaccine to spread through the population. We build a mathematical model to test whether a RTV can facilitate disease management in instances where reversion is likely to introduce the vector into the population or when the vector organism is already established in the host population, and the vector and vaccine share perfect cross-immunity. Our results show that a RTV can autonomously eradicate a pathogen, or protect a population from pathogen invasion, when cross-immunity between vaccine and vector is absent. If cross-immunity between vaccine and vector exists, however, our results show that a RTV can substantially reduce the vaccination effort necessary to control or eradicate a pathogen only when continuously augmented with direct manual vaccination. These results demonstrate that estimating the extent of cross-immunity between vector and vaccine is a critical step in RTV design, and that herpesvirus vectors showing facile reinfection and weak cross-immunity are promising.

Wildlife vaccination is an important tool for managing the burden of infectious disease in human populations, domesticated livestock and various iconic wildlife. Although substantial progress has been made in the field of vaccine designs for wildlife, there is a gap in our understanding of how to time wildlife vaccination, relative to host demography, to best protect a population. We use a mathematical model and computer simulations to assess the outcomes of vaccination campaigns that deploy vaccines once per annual population cycle. Optimal timing of vaccination is an important consideration in animals with short to intermediate life spans and a short birthing season. Vaccines that are deployed shortly after the birthing season best protect the host population. The importance of timing is greater in wildlife pathogens that have a high rate of transmission and a short recovery period. Vaccinating at the end of the birthing season best reduces the mean abundance of pathogen‐infected hosts. Delaying vaccination until later in the year can facilitate pathogen elimination. Policy Implications. Tuning wildlife vaccination campaigns to host demography and pathogen traits can substantially increase the effectiveness of a campaign. Our results suggest that, for a fluctuating population, vaccinating at, or shortly after, the end of the birthing season, best protects the population against an invading pathogen. If the pathogen is already endemic, delaying vaccination until after the birthing season is over can help facilitate pathogen elimination. Our results highlight the need to better understand and predict host demography in wildlife populations that are targeted for vaccination.

As the density of human and domestic animal populations increases, the threat of localized epidemics and global pandemics grows. Although effective vaccines have been developed for a number of threatening pathogens, manufacturing and disseminating vaccines in the face of a rapidly spreading epidemic or pandemic remains a formidable challenge. One potentially powerful solution to this problem is the use of transmissible vaccines. Transmissible vaccines are capable of spreading from one individual to another and are currently being developed for a range of infectious diseases. Here we develop and analyze mathematical models that allow us to quantify the benefits of vaccine transmission in the face of an imminent or ongoing epidemic. Our results demonstrate that even a small amount of vaccine transmission can greatly increase the rate at which a naïve host population can be protected against an anticipated epidemic and substantially reduce the size of unanticipated epidemics if vaccination is initiated shortly after pathogen detection. In addition, our results identify key biological properties and implementation practices that maximize the impact of vaccine transmission on infectious disease.

Transmissible vaccines offer a revolutionary approach for controlling infectious disease and may provide one of the few feasible methods for eliminating pathogens from inaccessible wildlife populations. Current efforts to develop transmissible vaccines use recombinant vector technology whereby pathogen antigens are engineered to be expressed from innocuous infectious viral vectors. The resulting vaccines can transmit from host to host, amplifying the number of vaccine‐protected individuals beyond those initially vaccinated directly through parenteral inoculation. One main engineering challenge is the potential for natural selection to favor vaccine mutants that eliminate or reduce expression of antigenic inserts, resulting in immunogenic decay of the vaccine over time. Here, we study a mathematical model of vector mutation whereby continuous elimination of the antigenic insert results in reversion of the vaccine back into the insert‐free vector. We use this model to quantify the maximum allowable rate of reversion that can be tolerated for a transmissible vaccine to maintain a critical threshold level of immunogenicity against a target pathogen. Our results demonstrate that even for transmissible vaccines where reversion is frequent, performance will often substantially exceed that of conventional, directly administered vaccines. Further, our results demonstrate the feasibility of designing transmissible vaccines that yield desired levels of immunogenicity, yet degrade at a rate sufficient for persistence of the recombinant vaccine within the environment to be minimized.

Lassa virus is a significant burden on human health throughout its endemic region in West Africa, with most human infections the result of spillover from the primary rodent reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a Bayesian methodology for estimating epidemiological parameters of Lassa virus within its rodent reservoir and for generating probabilistic predictions for the efficacy of rodent vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa virus surveillance data from rodent populations. Using simulated data, we show that our method accurately estimates key model parameters, even when surveillance data are available from only a relatively small number of points in space and time. Applying our method to previously published data from two villages in Guinea estimates the time-averaged R 0 of Lassa virus to be 1.74 and 1.54 for rodent populations in the villages of Bantou and Tanganya, respectively. Using the posterior distribution for model parameters derived from these Guinean populations, we evaluate the likely efficacy of vaccination programs relying on distribution of vaccine-laced baits. Our results demonstrate that effective and durable reductions in the risk of Lassa virus spillover into the human population will require repeated distribution of large quantities of vaccine.

32Lassa virus is a significant burden on human health throughout its endemic region in 33 West Africa, with most human infections the result of spillover from the primary rodent 34 reservoir of the virus, the natal multimammate mouse, M. natalensis. Here we develop a 35 Bayesian methodology for estimating epidemiological parameters of Lassa virus within its 36 rodent reservoir and for generating probabilistic predictions for the efficacy of rodent 37 vaccination programs. Our approach uses Approximate Bayesian Computation (ABC) to 38 integrate mechanistic mathematical models, remotely-sensed precipitation data, and Lassa 39 virus surveillance data from rodent populations. Using simulated data, we show that our 40 method accurately estimates key model parameters, even when surveillance data are available 41 from only a relatively small number of points in space and time. Applying our method to 42 previously published data from two villages in Guinea estimates the time-averaged of Lassa 0 43 virus to be 1.658 and 1.453 for rodent populations in the villages of Bantou and Tanganya, 44 respectively. Using the posterior distribution for model parameters derived from these 45 Guinean populations, we evaluate the likely efficacy of vaccination programs relying on 46 distribution of vaccine-laced baits. Our results demonstrate that effective and durable 47 reductions in the risk of Lassa virus spillover into the human population will require repeated 48 distribution of large quantities of vaccine. 49 3 50 Author Summary 51 Lassa virus is a chronic source of illness throughout West Africa, and is considered to be a threat 52 for widespread emergence. Because most human infections result from contact with infected 53 rodents, interventions that reduce the number of rodents infected with Lassa virus represent 54 promising opportunities for reducing the public health burden of this disease. Evaluating how 55 well alternative interventions are likely to perform is complicated by our relatively poor 56 understanding of viral epidemiology within the reservoir population. Here we develop a novel 57 statistical approach that couples mathematical models and viral surveillance data from rodent 58 populations to robustly estimate key epidemiological parameters. Applying our method to 59 existing data from Guinea yields well-resolved parameter estimates and allows us to simulate a 60 variety of rodent vaccination programs. Together, our results demonstrate that rodent 61 vaccination alone is unlikely to be an effective tool for reducing that public health burden of 62 Lassa fever within West Africa. 63 65 Lassa virus is a zoonotic pathogen endemic to West Africa where it poses a significant 66 burden on human health (Buckley et al. 1970). Although only a relatively small proportion of 67 human cases result in severe symptoms and mortality, human infection is common, with 8-52% 68 of the human population within Sierra Leone seropositive (McCormick et al. 1987; Bausch et al. 69 2001; Dan-Nwafor et al. 2019), indicative of past...

Zoonotic pathogens such as Ebola and rabies pose a major health risk to humans. One proven approach to minimizing the impact of a pathogen relies on reducing its prevalence within animal reservoir populations using mass vaccination. However, two major challenges remain for vaccination programs that target free-ranging animal populations. First, limited or challenging access to wild hosts, and second, expenses associated with purchasing and distributing the vaccine. Together, these challenges constrain a campaign’s ability to maintain adequate levels of immunity in the host population for an extended period of time. Transmissible vaccines could lessen these constraints, improving our ability to both establish and maintain herd immunity in free-ranging animal populations. Because the extent to which vaccine transmission could augment current wildlife vaccination campaigns is unknown, we develop and parameterize a mathematical model that describes long-term mass vaccination campaigns in the US that target rabies in wildlife. The model is used to investigate the ability of a weakly transmissible vaccine to (1) increase vaccine coverage in campaigns that fail to immunize at levels required for herd immunity, and (2) decrease the expense of campaigns that achieve herd immunity. When parameterized to efforts that target rabies in raccoons using vaccine baits, our model indicates that, with current vaccination efforts, a vaccine that transmits to even one additional host per vaccinated individual could sufficiently augment US efforts to preempt the spread of the rabies virus. Higher levels of transmission are needed, however, when spatial heterogeneities associated with flight-line vaccination are incorporated into the model. In addition to augmenting deficient campaigns, our results show that weak vaccine transmission can reduce the costs of vaccination campaigns that are successful in attaining herd immunity.