# Plant pathogen profiling with the EpiPvr package

Ruairí Donnelly, Israël Tankam-Chedjou, Christopher A. Gilligan

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Ruairí Donnelly, Israël Tankam-Chedjou, Christopher A. Gilligan. Plant pathogen profiling with the EpiPvr package. Methods in Ecology and Evolution, 2025, 10.1111/2041-210x.70219. hal-05453674

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# Plant pathogen profiling with the EpiPvr package

Ruairí Donnelly<fnref n="1" /> | Israël Tankam Chedjou<fnref n="2" /> | Christopher Aidan Gilligan<fnref n="1" />

**Correspondence**

Ruairí Donnelly

Email: rd501@cam.ac.uk

**Funding information**

Bill and Melinda Gates Foundation

Handling Editor: Laura Jane Graham

## Abstract

1. This study introduces a flexible framework for epidemiological profiling of insect-borne plant pathogens (IBPPs), utilising readily available experimental data. The framework is applicable to most IBPPs transmitted by insects feeding on plant veins, with particular relevance to whitefly-borne viruses that impact cassava production in sub-Saharan Africa. The goal of the study was to provide an approach to estimate critical parameters for IBPP epidemics, and to use these estimates to assess epidemic risk in the field.

2. The study employs analyses of access period experimental data to estimate three key parameters underlying IBPP epidemics: (i) the rate of pathogen acquisition by insects, (ii) the rate of plant inoculation by pathogen-carrying insects and (iii) the rate of loss of infectiousness for pathogen-carrying insects. These parameters are incorporated into models that allow for the inference of epidemic risk following inoculum introduction in the field. The methods are packaged into the EpiPvr R package, which facilitates rapid implementation and analysis.

3. The EpiPvr R package was applied to analyse whitefly-transmitted cassava viruses. The results show that a critical whitefly density of approximately greater than 4 per plant is needed for sustained spread of cassava brown streak ipomovirus (henceforth, CBSI) from infected planting material. In contrast, CBSI introductions in whitefly are liable to go extinct even in high-density whitefly populations. A different picture is uncovered for cassava mosaic begomovirus (henceforth, CMB)—whereby introductions in both plants and whitefly are found to be viable even at very low whitefly densities. This demonstrates significant, actionable, differences in the transmission attributes of these viruses—as uncovered by the EpiPvr package.

4. These findings highlight the utility of the EpiPvr framework for predicting the outcome of pathogen introductions and for guiding targeted disease management strategies. The ability to estimate key parameters and predict epidemic risk enables more informed decision-making for the control of insect-transmitted plant diseases, with broader applications for managing plant pests globally in both natural and cultivated systems.

This is an open access article under the terms of the [Creative Commons Attribution](https://creativecommons.org/licenses/by) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society.

<footnote n="1">¹Epidemiology and Modelling Group,
Department of Plant Sciences, University
of Cambridge, Cambridge, UK</footnote>

<footnote n="2">²Institut Agro, Univ Rennes, INRAE,
IGEPP, Rennes, France</footnote>
<figure><img src="image_3.png" /></figure>

KEYWORDS

insect vectors, parameter estimation, plant epidemiology, plant viruses

# 1 | INTRODUCTION

Global productivity from the cultivation of crops like cassava is severely limited by insect-borne plant pathogens (henceforth IBPPs) (Colvin et al., 2006). For instance, cassava provides more than half of the dietary calories for over 200 million people in East and Central Africa (Alene et al., 2013; FAO & IFAD, 2005; Reincke et al., 2018; Mwebaze et al., 2024), but African cassava production has been severely impacted by whitefly-borne viruses resulting in food insecurity (Mwebaze et al., 2024) and > $1.25 billion in annual crop losses (Legg et al., 2006; Macfadyen et al., 2021; Mwebaze et al., 2024). Researchers in insect-borne plant virology typically use a set of laboratory experiments referred to here as *access period assays* to confirm, and to investigate, virus transmission by putative insect vectors (Anhalt & Almeida, 2008; Chant, 1958; Dubern, 1994; Ingwell et al., 2012; Maruthi et al., 2016; Moreno-Delafuente et al., 2013). In this paper, we introduce a framework for *epidemiological profiling* of IBPPs using access period data and provide the corresponding functions in the dedicated R package EpiPvr (available on the CRAN repository; see Donnelly, 2025). By *epidemiological profiling*, we mean estimation of virus transmission parameters and subsequent inference of epidemic risk. This paper describes the functions of the EpiPvr package and applies them to profile two whitefly-transmitted viruses: cassava mosaic begomovirus (CMB) and cassava brown streak ipomovirus (CBSI).

The epidemiology of IBPP transmission is influenced by the rates of virus acquisition and inoculation, and the retention period in the insect also plays an important role. Indeed, retention duration is a common means of classifying plant viruses as persistently transmitted (PT, long retention) and semi- or non-persistently transmitted (SPT or NPT, short retention) (Eigenbrode et al., 2018; Hogenhout et al., 2008)—with the latent period relevant for PT but not for SPT or NPT viruses, where the latent period is expected to be of low or zero magnitude (Hogenhout et al., 2008). Note that true NPT viruses are acquired from epidermal plant cells by aphids (Carr et al., 2018)—it is important to note that at present our framework does not apply to these viruses, which require separate treatment (Donnelly et al., 2019), and instead applies to the majority of IBPPs that are acquired from the phloem of plants (Carr et al., 2018). Among these phloem-restricted viruses are SPT viruses like CBSI and PT viruses like CMB. The functions in *EpiPvr* provide a user-friendly means to estimate the rates of virus acquisition and inoculation from access period data provided by the user.

Our approach is built around simple probability theory, which is central to modern epidemiology because of its dual utility in parameter estimation and statistical inference (Keeling & Rohani, 2011). In

the first of these dual uses, probability models for epidemiological scenarios can be combined with epidemiological data to estimate parameters (Bolker, 2008). In the *EpiPvr* package, probability models for access period assays are combined with user-inputted access period assay data to estimate acquisition, inoculation and virus clearance rates. In the second of these dual uses, probability models based upon parameter estimates can be used to make inferences. In the *EpiPvr* package, virus parameter estimates are used to infer the epidemic risk from inoculum introductions—in insects or alternatively in plants—in the field.

For cassava viruses, and viruses of roots and tubers in general, the propagation of infected planting material for new growth, for example through stem cuttings in the case of cassava, constitutes an additional transmission mode in the field. This mode of transmission is influential in large-scale spatio-temporal dynamics of IBPPs, but is not a factor in the question of whether or not the introduction of an infected plant or an infected insect vector into a population of host plants will result in an epidemic apart from as an epidemic seeding event. This is because in the absence of preferential selection of infected plant material, propagation alone cannot lead to epidemic growth at a given location, but rather propagation plays an important role in the persistence of infection across growing seasons as well as virus movement to new locations. Note, however, that the initial introduction of an infected plant can be thought of as representing a single transmission event based on the introduction of propagated infected material.

Experiments in insect-borne transmission are important, yet to date there has been no obvious way to translate laboratory data into epidemic risk. Experimenters tend to interpret their response data in terms of qualitative patterns, noting, for example, the number of insects in cohorts needed for high transmissibility; the minimum periods needed for acquisition and inoculation, etc. (Dubern, 1994). To our knowledge, only limited parameter estimation has been applied to AP experimental data (i.e. mathematical point estimates in Donnelly & Gilligan, 2020 and infectiousness estimates in Donnelly & Gilligan, 2023), and therefore, there has been no way to extrapolate to field risk. In this paper, we provide this missing link together with a dedicated R package. The paper is structured as follows: we first introduce the key epidemiological processes for phloem-limited IBPPs. We then describe the estimation of virus parameters using models based on these processes from access period data using Bayesian analysis. We next introduce the inference of local epidemic risk from the viral parameter estimates and several additional local parameters. We show how to apply the methods to published data as illustrated by whitefly-borne cassava viruses. The *EpiPvr* package enables rapid adoption of the epidemiological profiling to analyse and identify effective strategies for disease management.
## 2 | MATERIALS AND METHODS

Access period experiments are an essential means to study insect vector transmission of plant pathogens in the laboratory (Figure 1). In these experiments, insect vector cohorts are provided feeding access to pathogen-infected plants, and are then transferred to healthy test plants. This article introduces a framework that makes use of access period experimental data to produce epidemiological profiles of plant pathogens. Two steps lie at the heart of the framework: parameter estimation from experimental data (step A) and the subsequent inference of field epidemic probabilities (step B) (together referred to as epidemiological profiling). Both steps (laboratory vs. field model) are based on a quantitative description of the IBPP interaction. In the first step, we tailor a simple version of the quantitative model (referred to as a laboratory model) to generic access period assays for the estimation of virus transmission parameters. In the second step, we use the parameter estimates with a probability model of field introductions (field model) to make inferences relating to epidemic risk.

### 2.1 | How insects transmit the virus

For phloem-restricted plant viruses like the cassava viruses CMB and CBSI, the larger the period of feeding, the greater the rate of virus acquisition and inoculation (Figure 2a). This means that the rates of acquisition and inoculation for a given host plant are proportional to the number of uninfected and infected insect vectors, respectively, feeding on the phloem of the plant.

$$ \text{Acquisition (models: field; lab.)} \qquad I_j^F \xrightarrow{(F-j)\alpha} I_{j+1}^F \qquad (1) $$

$$ \text{Inoculation (models: field; lab.)} \qquad S_j^F \xrightarrow{j\beta} E_j^F \qquad (2) $$

where $\alpha$ and $\beta$ in Expressions (1) and (2) denote the acquisition and inoculation rates, and where $X_j^F$ denotes a plant state (see Figure 1 and Table S1.1 for an overview and a complete list of parameters, respectively, and see Figure 2 for experiment schematic) with $X \in \{S, E, I\}$ (susceptible, S, virus-exposed, E or infectious, I), with a number of

### Access period experiments and EpiPvr terminology

<figure><img src="image_4.png" /></figure>

* In access period experiments, an insect vector cohort is provided feeding access to a pathogen-infected plant and is then transferred to a healthy test plant - for each replicate conducted. Ultimately, the number of test plants becoming infected is recorded as a proportion of the number of assay replicates.

* As such, access period experiments consist of two or more sub-assays. Each assay of the experiment shares a common structure: an acquisition access period (AAP) followed by an inoculation access period (IAP) (above schematic). For persistently-transmitted pathogens (PT) an intermediate feeding period is also provided to allow for insect progression through a latent infected state (LAP).

**Acquisition varying sub-assay:** in which $T^A$ is varied ($T^A$ above) and $T^I$ is fixed.

**Inoculation varying sub-assay:** in which $T^I$ is varied ($T^I$ above) and $T^A$ is fixed.

<caption>FIGURE 1 Terminology for the laboratory model of the insect-borne plant pathogen (IBPP) access period experiment. Note that further details relating to, for example, $T^A$ and other terms appear in the model schematic, see Figure 3a. Note also that following acquisition access, insects are immediately moved to the subsequent plant for inoculation access. Nb. beyond the discipline of plant pathology (e.g. vector-borne human/animal pathology), similar experiments may sometimes be referred to as serial transmission experiments.</caption>
<figure><img src="image_5.png" /></figure>

Local movement of phloem-limited plant viruses

(a) HOW INSECT VECTORS TRANSMIT PLANT VIRUSES

<figure><img src="image_6.png" /></figure>

(b) HOW INSECT VECTORS DISPERSE PLANT VIRUSES

<figure><img src="image_7.png" /></figure>

FIGURE 2 Processes by which phloem-restricted insect-borne plant pathogens (IBPPs) undergo transmission and dispersal. In (a), virus acquisition (by virus-free insects from virus-infected plants) and virus inoculation (of healthy plants by virus-bearing insects) occur when insects feed on host plant phloem. The overall acquisition rate (see ai) is proportional to the number of virus-free insects that are feeding on virus-infected plants and the per-insect rate of virus acquisition, α. The overall inoculation rate (see aii) is proportional to the number of virus-bearing insects that are feeding on healthy plants and the per-insect rate of virus inoculation, β. Note that virus-exposed insects become infectious at rate γ (see ai inset) and virus-exposed plants become infectious at rate ν (see aii inset). In (b), insect life-history events alter the distribution of phloem-restricted IBPPs with virus-free and virus-bearing insects dispersing to new host plants at rate θ and with insect mortality loss occurring at rate b. In addition, virus-infected insects can clear the virus at rate μ becoming virus-free.

phloem-feeding insect vectors on the plant (integer-valued superscript F), of which a number are virus-carrying (integer-valued subscript j ≤ F). For instance, Expression (1) represents the population transition from an infected plant with j infected insect vectors (i.e. I<sup>F</sup><sub>j</sub>) to an infected plant with j + 1 infected insect vectors (i.e. I<sup>F</sup><sub>j+1</sub>) due to a virus acquisition event which occurs at rate (F − j)α. Note that the event rates (Expressions 1 and 2) are in units of per plant per day and are applicable when modelling both laboratory access period assays and field situations.

**2.2** | How insects disperse the virus

For phloem-restricted viruses like CMB and CBSI, the virus is ac-
quired when an uninfected insect feeds on infected phloem

(Figure 2b) and movement of an infected insect from 'birth' (i.e. ac-
quisition) to 'death' (i.e. insect death or viral clearance) depends on
the following life-history events with associated rates,

$$
\text{Infected insect dispersal (models: field)} \quad X_j^F \xrightarrow{j\theta} X_{j-1}^F, \quad S_0^F \xrightarrow{j\theta} S_1^F \quad (3)
$$

$$
\text{Infected insect death (models: field, } b_{y=f}; \text{ lab., } b_{y=l}) \quad X_j^F \xrightarrow{jb_v} X_{j-1}^F \quad (4)
$$

$$
\text{Infected insect recovery (models: field; lab.)} \quad X_j^F \xrightarrow{j\mu} X_{j-1}^F \qquad (5)
$$

where θ, bᵢ, bᵢ and μ are the per-insect rates of dispersal, field mortality, laboratory mortality and virus clearance, respectively, and where X ∈ {S, E, I}. Note that infected insect dispersal results in changes to both source and destination plant states. At
invasion all plants other than the inoculum, which itself originally arose from external means (i.e. through vegetative propagation or through infected insect migration into the field), are assumed to be free of infection (i.e. an invasion assumption as is standard in branching process formulations). For this reason, the destination plant is assumed to have state $S_0^F$ prior to infected insect dispersal. After dispersal has occurred the population may consist of two sets of inoculum (this occurs if the initial inoculum state was $I_j^F$ or $E_j^F$ with $j \ge 1$ or if it was $S_j^F$ with $j > 1$). In addition, a number of events influence the infectiousness of plants,

$$ \text{Exposed plant progression (models: field)} \quad E_j^F \xrightarrow{v} I_j^F \quad (6) $$

$$ \text{Exposed plant harvest (models: field)} \quad E_j^F \xrightarrow{h} S_j^F \quad (7) $$

$$ \text{Infected plant removal (models: field)} \quad I_j^F \xrightarrow{r} S_j^F \quad (8) $$

$$ \text{Infected plant harvest (models: field)} \quad I_j^F \xrightarrow{h} S_j^F \quad (9) $$

where $v$, $r$ are the per-plant rates of onset of infection and infected plant removal, and $h$ is the rate that plants are harvested (see Table S1.1 for complete list of parameters). The event rates (Expressions 3–9) are in units of per plant per day. Note also that Expressions (6–9) reflect an assumption of re-planting with susceptible material for simplicity. Note that a number of these events (Equations 3 and 6–9) apply only to field situations; that is, they are not relevant to the laboratory context.

## 2.3 | Step A, estimating viral transmission in the lab

Two parameters, $\alpha$ (acquisition rate) and $\beta$ (inoculation rate), play a central role in IBPP transmission in the field as well as in laboratory access period assays, and a third, $\mu$ (insect virus clearance rate) is critical to IBPP epidemiology (Equations 1 and 2). Estimating these parameters is a key task. It is also important to account for virus latent period in the insect where relevant in order to ensure reliable parameter estimation (through the rate with which virus-exposed insects become infectious, $\gamma$). A simplified representation of access period assays is shown in Figure 3a. For PT viruses the assay is frequently extended to include a latent access period (e.g. Dubern (1994)'s PT virus assay with—cf. Maruthi et al. (2016)'s SPT virus assay without—latent access period). As outlined in Figure 3a, parameter estimation from access period data requires calculation of the probability of the data given a probability model of the assay. In effect, the model calculates how likely the observed data are for different values of the underlying parameters given assay access durations (see Supporting Information S1 and S2 for details). In this way, sources of variation (assay variation and natural variation) that underlie access period data can be harnessed to estimate event rates, such as $\alpha$, $\beta$, $\mu$ and $\gamma$ using Bayesian analysis.

## 2.4 | Step B, inferring epidemic probability in the field

Note that throughout this subsection the following assumptions are made: (i) space is implicit, that is while the model considers within-field insect dispersal between plants, spatial distance is not treated explicitly; (ii) the insect burden on plants is assumed to be constant (see Section 4 and below for further details).

In step B the virus parameter estimates for $\alpha$, $\beta$ and $\mu$ from step A are used to produce estimates for the epidemic probability (when 1 infected plant or 1 infected insect vector is introduced into a population of susceptible host plants). By epidemic probability, we mean the probability that introduced inoculum leads to local epidemic rather than local extinction of the pathogen. Note that additional user-inputted local parameters are also required: the number of insects per plant (F), the insect dispersal ($\theta$) and natural mortality ($b_f$) rates, the harvesting rate ($h$) and the rate that infected plants are removed ($r$), and, in addition, the rate that exposed plants become infectious ($v$). Note that insect mortality is an important component of the insect infectious period and is therefore included in the modelling. Overall, for simplicity, the insect burden per plant is nevertheless assumed constant, that is an assumption of instantaneous replacement of dead insects with uninfected insects—see Supporting Information S1 for further detail on how this was implemented. The field model therefore assumes that a particular location or situation is associated with a constant insect burden such that all plants have F phloem-feeding insects. This assumption is highly suitable when plant pathologists have associated a location or situation with a given insect burden—or where the aim is to investigate the impact of the magnitude of vector density on epidemic risk. We now briefly indicate how the epidemic probability is calculated for IBPPs.

The strategy is to condition the epidemic probability from a given inoculum state on possible future events (Keeling & Rohani, 2011) (Figure 3b; Supporting Information S3). By inoculum state we mean the infection state of a single plant unit of infection. This may correspond to infection or latent infection of the plant and/or infection of any of the insects feeding on the plant. Furthermore, the number of possible inoculum states depends on the local insect burden, F: there are $2 \times (F + 1) + F$ such states ($I_0^F \dots I_F^F$, $E_0^F \dots E_F^F$ and $S_1^F \dots S_F^F$). The calculation involves relating the extinction probability for a given inoculum state to the extinction probabilities for other inoculum states. This is achieved by examining the inoculum states produced by the events that may occur and accounting for the extinction probabilities from these new states. This process, that is, accounting for extinction probabilities pre-, and post-, event occurrence, leads to a set of simultaneous equations that can be solved for the extinction probabilities associated with each inoculum state (and hence for epidemic probability, i.e. 1 – the probability of extinction).

In the following example, for illustration, we ask what is the fate of a single introduced infected plant $P(I_0^F)$. The possible future events for the inoculum state are as follows: the infected plant
## Probability models of the EpiPvr package

<figure><img src="image_8.png" /><figcaption>FIGURE 3 Probability models of the EpiPvr R package for laboratory and field scenarios in (a) and (b) respectively.</figcaption></figure>

may be rogued (i.e. transition from $I_0^F$ to $S_0^F$) with rate $r$, or it may be harvested (i.e. transition from $I_0^F$ to $S_0^F$) with rate $h$, or a phloem-feeding insect on the plant may acquire the virus (i.e. a transition from $I_0^F$ to $I_1^F$) with rate $F\alpha$. Conditioning on these possible events leads to $P(I_0^F) = (r/(r+h+F\alpha))P(S_0^F)+(h/(r+h+F\alpha))P(S_0^F)+(F\alpha/(r+h+F\alpha))P(I_1^F)$ in which the coefficients are the relative probabilities of a given event and the multiplicative terms are the probabilities of extinction for the ensuing inoculum states. Each of the inoculation states can be related to other inoculum states in the above manner. This leads to a set of simultaneous equations which can be solved for the extinction (hence epidemic) probabilities (Figure 3b).

## 2.5 | The EpiPvr R package

The EpiPvr R package has two main uses. The first is the Bayesian estimation of virus transmission rates from access period data

(step A in the previous section). This functionality uses MCMC (i.e. Markov-chain Monte Carlo algorithm) sampling through Stan (Stan Development Team, 2022). The second is the inference of epidemic probability based upon virus rate estimates and several local parameters (step B in the previous section). In what follows, we list the functions that are available in the EpiPvr R package,

1. The `estimate_virus_parameters_PT` function receives AP data for a given vector-PT virus-plant combination as user input together with assay configuration (i.e. AP feeding durations $T^A$, $T^L$, $T^I$ and the number of insect vectors used, $X_0$) and returns posterior parameter distributions for the transmission and epidemiological rates $\mu$, $\alpha$, $\beta$ and $\gamma$ (see Supporting Information S1 for details).

2. The `estimate_virus_parameters_SPT` function receives AP data for a given vector-SPT virus-plant combination as user input together with assay configuration (i.e. AP feeding durations $T^A$, $T^I$ and the number of insect vectors used, $X_0$) and returns posterior
parameter distributions for the transmission and epidemiological rates $\mu$, $\alpha$ and $\beta$ (see Supporting Information S2 for details).

3. The *calculate_epidemicProbability* function receives event rate parameters for virus transmission and epidemiology ($\mu$, $\alpha$, $\beta$) as well as local parameters $F$, $\theta$, $r$, $h$ and $b_t$ as user input, and returns epidemic probability for different types of inoculum state (see Supporting Information S3 for details).

4. The *AP_data_simulator* function receives event rate parameters for virus transmission and epidemiology ($\mu$, $\alpha$, $\beta$) as well as assay feeding durations and returns simulated access period data (see Supporting Information S4 for details of the statistical simulation process).

## 3 | RESULTS

In what follows we describe the epidemiological profiling of the whitefly-borne CMB and CBSI cassava viruses. We first report the viral parameter estimates, and then the risk inferences, as described in the methods section.

### 3.1 | Profiling whitefly-borne cassava viruses

When we applied the laboratory-scale model (materials and methods) to the Dubern (1994) CMB dataset (Supporting Information S1) and the Maruthi et al. (2016) CBSI dataset (Supporting Information S2), we obtained 95% credible intervals for CMB and CBSI acquisition, inoculation and insect clearance rates (Table 1a,b). In addition, we estimated the latent progression rate for the PT CMB virus—but note that the use of an alternative plant for the LAP (i.e. latent access period) meant that we were unable to utilise the latent period varying sub-assay from Dubern (1994) in our model fitting exercise (see subsection 'The latent period varying sub-assay in Dubern (1994)' Supporting Information S1).

Model convergence was assessed by examining potential scale reduction factors ($Rhat \le 1.01$ for all parameters for both CMB and CBSI), effective sample sizes ($n_{eff} > 10$). Iterations for all parameters for both CMB and CBSI) and the absence of divergent transitions

or tree depth exceedences (i.e. when the MCMC sampling goes off-track, and when the sampler's path is longer than allowed, respectively), which are basic measures of sampling stability. Absence of strong correlations among the estimated parameters was confirmed by examination of parameter pairs plots and mixing of chains confirmed by the earlier reported effective sample sizes. Excellent agreement between observed data and forward simulation from the estimated parameters is evident from Figure S2.2 for CBSI and good agreement for CMB (Figure S1.3 in which open circles showing the empirical data fall between the black curves showing 95% credible intervals from the forward simulation). Excellent model fit, as evaluated through Bayesian $R^2$, was confirmed for CBSI (70% of variance explained by model) and good model fit for CMB (≈ 50% of variance explained by model). Note that CMB modelling, though good, was not as strong as for CBSI—this likely relates to the use of an alternative plant (Chinese lantern rather than cassava) for the intermediate LAP phase in the CMB AP data of Dubern (1994), as discussed in Supporting Information S1.

Viral parameter estimates (full posterior distributions) were then taken forward for both viruses to calculate local epidemic probability (summarised in Table 2) corresponding to set levels of insect burden. The epidemic probabilities that resulted are summarised in Table 2, in which posterior credible intervals for epidemic probability correspond to insect burden for viral introduction in plants (A) and insects (B). In addition, full posterior distributions are shown in Figure 4 focusing on plant cf. insect inocula (plant and insect forms of inoculum for selected values of insect burden: $F=1$ vs. $F=3$ for CMB in A vs. C, and $F=4$ vs. $F=10$ for CBSI in B vs. D). Full posterior distributions are also shown in Figure 5 focusing on insect burden variation. We now collate these findings as epidemiological profiles for CBSI and then for CMB.

### 3.2 | CBSI: A feeble inoculator that hides in plain sight

* Highly ephemeral retention in *Bemisia tabaci* insect vector: 95% credible interval for virus clearance per hour, 0.406 – 1.468 $h^{-1}$ (Table 1aiv); median virus retention $0.807^{-1} = 1.24h$.

<table><caption>TABLE 1 Parameter estimates for viral transmission rates using the *estimate_virus_parameters_SPT* (a) and *estimate_virus_parameters_PT* (b) functions.</caption><thead><tr><th rowspan="2">Rate parameter</th><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">(a) CBSI</th><th colspan="3">(b) CMB</th></tr><tr><th>Median</th><th>2.5%</th><th>97.5%</th><th>Median</th><th>2.5%</th><th>97.5%</th></tr></thead><tbody><tr><td>(i)</td><td>Acquisition</td><td>α</td><td>0.638 h<sup>-1</sup></td><td>0.088 h<sup>-1</sup></td><td>1.735 h<sup>-1</sup></td><td>0.014 h<sup>-1</sup></td><td>0.012 h<sup>-1</sup></td><td>0.016 h<sup>-1</sup></td></tr><tr><td>(ii)</td><td>Inoculation</td><td>β</td><td>0.056 h<sup>-1</sup></td><td>0.019 h<sup>-1</sup></td><td>0.475 h<sup>-1</sup></td><td>2.96 h<sup>-1</sup></td><td>2.219 h<sup>-1</sup></td><td>3.995 h<sup>-1</sup></td></tr><tr><td>(iii)</td><td>Latency</td><td>γ</td><td>—</td><td>—</td><td>—</td><td>0.75 h<sup>-1</sup></td><td>0.317 h<sup>-1</sup></td><td>2.053 h<sup>-1</sup></td></tr><tr><td>(iv)</td><td>Insect clearance</td><td>μ</td><td>0.807 h<sup>-1</sup></td><td>0.406 h<sup>-1</sup></td><td>1.468 h<sup>-1</sup></td><td>0.004 h<sup>-1</sup></td><td>0.0001 h<sup>-1</sup></td><td>0.019 h<sup>-1</sup></td></tr></tbody></table>

Note: Parameter estimates are shown for whitefly-borne CBSI as calculated from the acquisition and inoculation access period assay datasets of Maruthi et al. (2016) (a), and for whitefly-borne CBSI as calculated from the acquisition and inoculation access period assay datasets of Dubern (1994) (b) (see Supporting Information S1 and S2 for further details). Table entries relate to 95% credible intervals for parameter distributions.
<table><caption>TABLE 2 Epidemiological field inferences for whitefly-borne CMB and CBSI using the *epidemicProbability* function (materials and methods, step B), illustrated with CBSI (top) and CMB (bottom).</caption><thead><tr><th rowspan="2"></th><th rowspan="2"></th><th rowspan="2"></th><th colspan="3">(a) Plant inoculum</th><th colspan="3">(b) Insect inoculum</th></tr><tr><th>Median</th><th>2.5%</th><th>97.5%</th><th>Median</th><th>2.5%</th><th>97.5%</th></tr></thead><tbody><tr><td rowspan="3">CMB</td><td>(i)</td><td>F = 1</td><td>0.774</td><td>0.710</td><td>0.810</td><td>0.835</td><td>0.758</td><td>0.865</td></tr><tr><td>(ii)</td><td>F = 2</td><td>0.916</td><td>0.896</td><td>0.929</td><td>0.927</td><td>0.904</td><td>0.936</td></tr><tr><td>(iii)</td><td>F = 3</td><td>0.933</td><td>0.912</td><td>0.945</td><td>0.920</td><td>0.896</td><td>0.930</td></tr><tr><td rowspan="3">CBSI</td><td>(iv)</td><td>F = 4</td><td>0.297</td><td>0.133</td><td>0.442</td><td>0.020</td><td>0.006</td><td>0.092</td></tr><tr><td>(v)</td><td>F = 7</td><td>0.629</td><td>0.534</td><td>0.710</td><td>0.040</td><td>0.021</td><td>0.197</td></tr><tr><td>(vi)</td><td>F = 10</td><td>0.761</td><td>0.695</td><td>0.816</td><td>0.048</td><td>0.027</td><td>0.238</td></tr></tbody></table>

Note: See Table 1 for parameter estimates representing virus transmission that were passed as arguments to the *epidemicProbability* function together with the following local parameter values: $\theta = 0.45 \text{ day}^{-1}$, $r = 1/28 \text{ day}^{-1}$, $h = 1/365 \text{ day}^{-1}$, $b = 1/14 \text{ day}^{-1}$, $v = 1/14 \text{ day}^{-1}$ (see Table S1.1 for a list of parameters). Table entries relate to 95% credible intervals for epidemic probability distributions.

<figure><img src="image_9.png" /><figcaption>FIGURE 4 Risk inference for cassava viruses depending on form of inoculum. Posterior distributions for the epidemic probability parameter for cassava mosaic begomovirus (CMB) (left, a and c) and cassava brown streak ipomovirus (CBSI) (right, b and d) when inoculum arrives in the form of an infected plant or insect for different background levels of insect burden, *F*. Posterior distributions are presented for a single level of insect abundance: *F* = 1 (CMB) or *F* = 4 (CBSI), top, a and b; *F* = 3 (CMB) or *F* = 10 (CBSI), bottom, c and d. See Supporting Information S1-S3 for further details of the model fitting and see Table S1.1 for a list of parameters. Figure data-points were obtained using a combination of the function calls *estimate_virus_parameters_SPT*, *estimate_virus_parameters_PT* and *calculate_epidemicProbability* functions.</figcaption></figure>

* Highly transmissible from infected cassava to uninfected *B. tabaci*: 95% credible interval for acquisition rate per hour, 0.088 – 1.735 h⁻¹ (Table 1ai); median probability of acquisition per insect in a 24 h feeding period ≈ 1.

* Low transmissibility from infected *B. tabaci* to uninfected cassava: 95% credible interval for inoculation rate per hour, 0.019 – 0.475 h⁻¹ (Table 1ai). Thus, while median probability of inoculation per virus-bearing insect in a 24 h feeding period is

≈ 0.7, median inoculation probability per insect infection is only ≈ 0.07—when we take account of median retention time.

* Low epidemic risk from insect inocula, high epidemic risk from plant inocula requires high local insect burden: High likelihood of local epidemics given infected plant introduction but only for moderate-high *B. tabaci* insect burden (95% epidemic probability credible interval 0.133 – 0.442 for four whitefly per top five leaves, cf. 0.695 – 0.816 for 10 whitefly, Table 2aiv cf., Table 2avi). Low
<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

FIGURE 5 Risk inference for cassava viruses depending on insect burden. Posterior distributions for the epidemic probability parameter for cassava mosaic begomovirus (CMB) (left, a and c) and cassava brown streak ipomovirus (CBSI) (right, b and d) for various background levels of insect burden, $F \in \{1,2,3\}$ (CMB) and $F \in \{4,7,10\}$ (CBSI). Posterior distributions are presented for inoculum arriving in the form of an infected plant (top, a and b) or in the form of an infected insect (bottom, c and d). See Supporting Information S1-S3 for further details of the model fitting and see Table S1.1 for a list of parameters. Figure data-points were obtained using a combination of the function calls `estimate_virus_parameters_SPT`, `estimate_virus_parameters_PT` and `calculate_epidemicProbability` functions.

likelihood of local epidemics given infected insect introduction (95% credible interval for epidemic probability, 0.027 – 0.238 even for 10 whitefly per top 5 leaves, Table 2bvi);

* *Low symptom detectability in plants:* Beyond the scope of access period experiments—CBSI is notoriously difficult to identify in over-ground biomass. As such, there is a high tendency for human-mediated propagation to new seasons and new locations of cassava cultivation. Thus it has the ability to persist beyond the growth period of a cassava population.

## 3.3 | CMB: An all-rounder that is hard to miss

* *Weak- to moderately transmissible from infected cassava to uninfected *B. tabaci*: 95% credible interval for acquisition rate per hour, 0.012 – 0.016 $h^{-1}$ (Table 1bi); median probability of acquisition per uninfected insect in a 24 h feeding period $\approx$ 0.285.

* *Highly transmissible from infected *B. tabaci* to uninfected cassava:* 95% credible interval for inoculation rate per hour, 2.219 – 3.995 $h^{-1}$ (Table 1bii); median probability of at least one inoculation per infected insect in a 24 h feeding period $\approx$ 1.

* *Sustained retention in the *B. tabaci* insect vector:* 95% credible interval for loss of insect infectiousness per hour, 0.0001 – 0.019 $h^{-1}$ (Table 1biv) (corresponding to between 2.2 and >100 days). This is supported by the finding of no evidence for whitefly clearance of CMB in Donnelly and Gilligan (2023).

* *Very high risk from plant and insect introductions:* High likelihood of local epidemics given inoculum introductions in plant or in insect for even relatively low *B. tabaci* insect burdens. 95% credible interval for epidemic probability, 0.710 – 0.810 (plant inoculum) and 0.758 – 0.865 for one whitefly per top five leaves (Table 2ai,bi).

* *High symptom detectability in plants:* Beyond the scope of access period experiments—CMB disease is highly visible in over-ground biomass. As such, there is greater scope for managing human-mediated propagation to new seasons and new locations of cassava cultivation. The release of CMB-tolerant varieties, however, in recent decades may have led to chronic and less visible CMB disease incidence.

## 3.4 | Computational validation of epidemic risk

For comprehensiveness, we also used individual-based simulation to compare predicted values of epidemic probability (*calculate_epidemicProbability* function, EpiPvr package) with the outcome of a large number of simulations. By individual-based simulation we mean the reproduction of the events that occur (on a per insect, per plant basis) when inoculum is introduced into a field, with events simulated in proportion to event rates that are updated each time an event occurs. Correspondence between prediction methods and simulation is achieved by introducing an infected host into a field of susceptible hosts at the start of a season—with fields evaluated
<figure><img src="image_12.png" /></figure>

at the end of the season for epidemic growth or extinction of the inoculum. This procedure provides a baseline for verifying the accuracy of our methods.

In brief, we found that the predicted epidemic probability matched the outcomes of simulations across a range of parameter value sets (Figure S5.1, blue crosses for predicted epidemic probabilities match circles for simulated epidemic probabilities). The exercise also demonstrated that infrequent removal of infectious plants (> 2 weeks longevity of symptomatic plants) are associated with high epidemic probabilities, that low whitefly dispersal (< 1 dispersal per day per whitefly) lead to rapid declines in epidemic probability, that epidemic probability was less than 0.5 only for very rapid loss of infectiousness (< 2 h retention), and that epidemic probability was less than 0.5 only for very few (i.e. around 5) whitefly per top five leaves (Figure S5.1A-D, respectively; note that *B. tabaci* are typically active on the top five leaves of cassava plants).

## 4 | DISCUSSION

We have introduced a framework to estimate virus parameters and to predict the outcomes of plant virus introductions in the field (note that the framework is more broadly applicable to IBPPs in general with plant viruses the most numerous category). The framework utilises laboratory access period studies to estimate virus transmission parameters. Such studies are central to establishing insect transmission but say little about how transmissibility translates into epidemic risk. The framework that we introduce provides the missing link. Laboratory researchers can now extrapolate from their data to the consequences for insect-borne epidemics in field situations. We applied the methods to laboratory studies for CBSI and CMB, showing that CBSI is characterised by high virus acquisition rates but is hindered by a combination of moderate inoculation rate and highly ephemeral retention in the insect, while CMB is characterised by a high virus inoculation rate and a relatively low acquisition rate.

The sustained retention of CMB in *B. tabaci* relative to CBSI makes its ability to cause epidemics at low whitefly abundances far more favourable than for CBSI. This is consistent with claims that high regional whitefly abundance was needed for CBSI expansion in sub-Saharan Africa to occur (Donnelly & Gilligan, 2020). The combination of moderate inoculation and ephemeral retention leads to strikingly lower risk associated with CBSI-infected vector introductions than from CBSI-infected plant introductions. This is a key finding emerging from this work that is important for epidemic management. For CMB, epidemic risk is high from both types of introductions.

Plant pathologists use laboratory experiments on insect access to host plants to investigate IBPPs. The data from these experiments can be used to parameterise epidemic models. The wealth of published laboratory data from such experiments for a wide range of plant pathogens constitutes a valuable resource for epidemiologists—but they remain a relatively untapped resource. We encoded simple analyses of access period data—the proportion of infected

test plants in acquisition and inoculation varying sub-assays—in a dedicated R package—EpiPvr. The package estimates the main parameters of insect-borne transmission and then uses the estimates for an epidemiological profiling of the IBPP. The functions of the package can be run on a simple laptop: simple function calls complete in < 5 s; function calls on AP datasets completed in < 5 min. Note that while *calculate_epidemicProbability*() takes only seconds for a single parameter set; running the function on full posterior distributions scales with the number of points in the distribution (as a guideline running this function for 1000 MCMC chains took approx. < 1 h). In summary, the EpiPvr package provides plant pathologists with new laptop-based tools designed to expand their empirical investigations with quantitative results. The package can also facilitate new investigations, for example, to compare epidemic risk across host species and pathogens as well as for insect vector strains.

### 4.1 | Cassava viruses, management and future versions of the EpiPvr R package

Cassava is produced by smallholder farmers whose average cultivated area is less than 1 ha (Masamha et al., 2018) and is already mainly grown under inter-cropping systems (with crops, such as maize, legumes and bananas). It is important to understand the relationship of cassava inter-cropping and epidemics.

In general, epidemic growth is due to secondary transmission of insect-borne infection and this can be directly linked to the basic reproduction number, with sufficient insect-borne spread for epidemic growth occurring when the basic reproduction number $R_0 > 1$, which corresponds to non-zero epidemic probability. Our results show that the probability of CBSI epidemics is already relatively low in susceptible monocultures for infected insect introductions. This raises the possibility that CBSI epidemics arising from infected insect introductions could be entirely prevented using cultivar mixtures. A combination of strict control of the planting material that is propagated together with wide-spread intercropping of susceptible and resistant cultivars could entirely suppress landscape CBSI epidemics. A priority for future iterations of the EpiPvr R package will be the calculation of epidemic probability for crop mixtures to complement recent simulated epidemic results (Tankam Chedjou et al., 2025).

While the analyses of access period assays in the *EpiPvr* package take account of the assay insect density, the inference of epidemic probability in the field requires user input of insect abundance per plant. For plant pathologists using the package this is likely to be an acceptable requirement. Field plant pathologists, for instance, are likely to have typical abundances per plant in mind for specific locations. We aim to extend the methods, however, in future package versions to include functions that can predict insect abundance and hence epidemic probability based on meteorological data for a given location—where they are decisive environmental factors for the insect vector in question. In addition, the remaining non-cultural local parameters, $b_f$ (field insect mortality rate) and $\theta$ (dispersal rate) that currently require user input could also be calculated
from meteorological data. This will require insect vector life-history
data according to, for example, temperature which is already avail-
able for insect vectors of plant pathogens like whitefly (Aregbesola
et al., 2019, 2020).

4.2 | Uses of the EpiPvr package

A critical facet of managing cassava virus epidemics at the landscape
scale is the classification of cassava varieties in terms of their sus-
ceptibility to the virus in question. Such classification can be consid-
ered one of relative susceptibility; that is, classification will typically
take account of a reference susceptible cultivar. The functions of the
*EpiPvr* package can estimate virus transmission parameters from AP
assays for a reference susceptible cultivar, and also for a cultivar of
interest. Comparison of these parameter estimates alone can pro-
vide a quantitative means of classifying cultivars in terms of suscep-
tibility and resistance. In addition, MCMC methods can then provide
a statistical test of relative susceptibility or resistance by accounting
for variability in both sets of parameter estimates. Ultimately, rela-
tive susceptibility should be established in field trials—but such trials
tend to be multi-year, resource intensive, and can often produce am-
biguous results. Therefore, analysis of candidate cultivar AP assays
with the *EpiPvr* package could play a key role in streamlining and
interpreting field trials, and crucially can show breeders the impact
of a level of resistance of susceptibility in terms of epidemic risk.

A second facet of managing cassava virus risk is the establishment
of virus transmission for alternative virus and/or vector strains. While
AP assays have historically been important for this objective, the func-
tions of the *EpiPvr* package in combination with these assays provide
a means to quantify the parameters that were not previously possible
and to translate these experiments into local epidemic risk. Therefore,
where strain transmission has been demonstrated in the laboratory,
but where it is unclear how this relates to effective risk in the field, the
*EpiPvr* package can be used to quantify the relative risk involved.

4.3 | Modelling risk in a changing climate

Finally, where there is a risk of arrival of a plant virus that was not
previously present in a region, the *EpiPvr* package provides a means
to quantify local epidemic risk. This exercise is widely conducted by
countries and regions as a part of pest risk assessment under WTO
rules. The risk inference part of the *EpiPvr* package is particularly
suitable for modelling the risk of local pest establishment—a key
stage in pest risk assessments (Leung et al., 2012; World Trade
Organization, 1995).

5 | CONCLUSIONS

In summary, we provide a framework to harness the wealth of pub-
lished access period data that are available for IBPPs. The methods

are assembled in the EpiPvr R package (Donnelly, 2025) for direct
use by epidemiologists and by the plant pathologists who produce
these datasets. The package estimates IBPP transmission rates from
access period data and also uses inference of local epidemic prob-
ability upon introduction of infected material, which together we
refer to as epidemiological profiling. The methods provide actionable
findings relating to the management of cassava virus invasions, for
instance, by calculating the risk of local epidemics for a given level of
the insect vector. While other crops are projected to face significant
adaptation challenges in Africa to changing climates, cassava is re-
silient to climate change because of its drought tolerance (Mwebaze
et al., 2024) and may therefore undergo an increase in production
in the decades ahead. In sub-Saharan Africa, B. tabaci life histories
are generally favoured by warmer temperatures, though only up to
an upper thermal limit, and climate change could increase the risk of
cassava virus epidemics in some areas, while potentially reducing it
in others. The tools introduced in this paper will provide a means to
quantify changing epidemic threats to food security in a changing
world—for cassava viruses, and for IBPPs in general.

AUTHOR CONTRIBUTIONS

Ruairi Donnelly conceived and designed the methodology, con-
structed the code base, analysed the data and led the writing of the
manuscript. Ruairi Donnelly, Israël Tankam Chedjou and Christopher
Aidan Gilligan contributed to project design, article drafting and crit-
ical revisions. All authors gave final approval for publication.

ACKNOWLEDGEMENTS

The authors thank Frank Ndjomatchoua, Nik Cunniffe and Luca
Rossini for useful discussions. In addition, this work was performed
using resources provided by the Cambridge Service for Data Driven
Discovery (CSD3) operated by the University of Cambridge Research
Computing Service (www.csd3.cam.ac.uk), provided by Dell EMC
and Intel using Tier-2 funding from the Engineering and Physical
Sciences Research Council (capital grant EP/T022159/1), and DiRAC
funding from the Science and Technology Facilities Council.

FUNDING INFORMATION

This study was supported by the Gates Foundation (INV 010472: Improved Disease Monitoring & Management for Wheat and Cassava), which was also supported by the UK Foreign, Commonwealth & Development Office and INV-070408 (Knowledge Exchange of Epidemiological Work Product), which we gratefully acknowledge. In addition, Ruairi Donnelly acknowledges further support through an award from the BBSRC Flexible Talent Mobility Account.

CONFLICT OF INTEREST STATEMENT

All authors confirm they have no conflict of interest.

DATA AVAILABILITY STATEMENT

Data and code supporting this article are archived on Zenodo: DOI:
https://doi.org/10.5281/zenodo.17681346 (Donnelly et al., 2025).
The original data were published in Dubern (1994) and Maruthi
et al. (2016). The EpiPvr R package is openly available on CRAN: https://cran.r-project.org/package=EpiPvr.

## STATEMENT ON INCLUSION

Our study brings together authors from different regions, including the region in which the example pathogens are most problematic. All authors were engaged early on with the research and study design to ensure that the diverse sets of perspectives they represent were considered from the onset. Whenever relevant, literature published by scientists from the region was cited.

## ORCID

Ruairí Donnelly https://orcid.org/0000-0001-7642-0317

## REFERENCES

Alene, A. D., Abdoulaye, T., Rusike, J., Labarta, R., Creamer, B., Del Río, M., Ceballos, H., & Becerra, L. A. (2013). Identifying crop research priorities based on potential economic and poverty reduction impacts: The case of cassava in Africa, Asia, and Latin America. *PLoS One*, 13(8), e0201803.

Anhalt, M. D., & Almeida, R. P. (2008). Effect of temperature, vector life stage, and plant access period on transmission of banana bunchy top virus to banana. *Phytopathology*, 98(6), 743–748.

Aregbesola, O. Z., Legg, J. P., Lund, O. S., Sigsgaard, L., Sporleder, M., Carhuapoma, P., & Rapisarda, C. (2020). Life history and temperature-dependence of cassava-colonising populations of *Bemisia tabaci*. *Journal of Pest Science*, 93, 1225–1241.

Aregbesola, O. Z., Legg, J. P., Sigsgaard, L., Lund, O. S., & Rapisarda, C. (2019). Potential impact of climate change on whiteflies and implications for the spread of vectored viruses. *Journal of Pest Science*, 15, 381–392.

Bolker, B. M. (2008). *Ecological models and data in R*. Princeton University Press.

Carr, J. P., Donnelly, R., Tungadi, T., Murphy, A. M., Jiang, S., Bravo-Cazar, A., Yoon, J. Y., Cunniffe, N. J., Glover, B. J., & Gilligan, C. A. (2018). Viral manipulation of plant stress responses and host interactions with insects. *Advances in Virus Research*, 102, 177–197.

Chant, S. R. (1958). Studies on the transmission of cassava mosaic virus by *Bemisia* spp. (*Aleyrodidae*). *Annals of Applied Biology*, 46(2), 210–215.

Colvin, J., Omongo, C. A., Govindappa, M. R., Stevenson, P. C., Maruthi, M. N., Gibson, G., Seal, S. E., & Muniyappa, V. (2006). Host-plant viral infection effects on arthropod-insect population growth, development and behavior: Management and epidemiological implications. *Advances in Virus Research*, 67, 419–452.

Donnelly, R. (2025). *EpiPvr: Estimating plant pathogen epidemiology parameters from laboratory assays*. R package version 0.0.1. https://doi.org/10.32614/CRAN/package.EpiPvr

Donnelly, R., Cunniffe, N. J., Carr, J. P., & Gilligan, C. A. (2019). Pathogenic modification of plants enhances long-distance dispersal of nonpersistently transmitted viruses to new hosts. *Ecology*, 100(7), e02725.

Donnelly, R., & Gilligan, C. A. (2020). What is pathogen-mediated insect superabundance? *Journal of the Royal Society Interface*, 17, 20200229.

Donnelly, R., & Gilligan, C. A. (2023). A new method for the analysis of access period experiments, illustrated with whitefly-borne cassava mosaic begomovirus. *PLoS Computational Biology*, 19(8), e1011291.

Donnelly, R., Tankam Chedjou, I., & Gilligan, C. A. (2025). Archived research data and code for "EpiPvr: Plant pathogen profiling" (v1.0.0). *Zenodo*. https://doi.org/10.5281/zenodo.17681346

Dubern, J. (1994). Transmission of African cassava mosaic geminivirus by the whitefly (*Bemisia tabaci*). *Tropical Science*, 34(1), 82–91.

Eigenbrode, S. D., Bosque-Pérez, N. A., & Davis, T. S. (2018). Insect-borne plant pathogens and their insects: Ecology, evolution, and complex interactions. *Annual Review of Entomology*, 63, 169–191.

FAO, & IFAD. (2005). A review of cassava in Africa with country case studies on Nigeria, Ghana, the United Republic of Tanzania, Uganda and Benin. *Proceedings of the Validation Forum on the Global Cassava Development Strategy* (Vol. 2).

Hogenhout, S. A., Ammar, E. D., Whitfield, A. E., & Redinbaugh, M. G. (2008). Insect vector interactions with persistently transmitted viruses. *Annual Review of Phytopathology*, 46(1), 327–359.

Ingwell, L. L., Eigenbrode, S. D., & Bosque-Perez, N. A. (2012). Plant viruses alter insect behavior to enhance their spread. *Scientific Reports*, 2(1), 578.

Keeling, M. J., & Rohani, P. (2011). *Modeling infectious diseases in humans and animals*. Princeton University Press.

Legg, J. P., Owor, B., Sseruwagi, P., & Ndunguru, J. (2006). Cassava mosaic virus disease in East and Central Africa: Epidemiology and management of a regional pandemic. *Advances in Virus Research*, 67, 355–418.

Leung, B., Roura-Pascual, N., Bacher, S., Heikkilä, J., Brotons, L., Burgman, M. A., Dehnen-Schmutz, K., Essl, F., Hulme, P. E., Richardson, D. M., & Sol, D. (2012). TEASing apart alien species risk assessments: A framework for best practices. *Ecology Letters*, 15(12), 1475–1493.

Macfadyen, S., Tay, W. T., Hulthen, A. D., Paull, C., Kalyebi, A., Jacomb, F., Parry, H., Sseruwagi, P., Seguni, Z., Omongo, C. A., & Kachigamba, D. (2021). Landscape factors and how they influence whitefly pests in cassava fields across East Africa. *Landscape Ecology*, 36, 45–67.

Maruthi, M. N., Jeremiah, S. C., Mohammed, I. U., & Legg, J. P. (2016). The role of the whitefly, *Bemisia tabaci* (Gennadius), and farmer practices in the spread of cassava brown streak ipomoviruses. *Journal of Phytopathology*, 31, 1–11.

Masamha, B., Thebe, V., & Uzokwe, V. N. (2018). Mapping cassava food value chains in Tanzania's smallholder farming sector: The implications of intra-household gender dynamics. *Journal of Rural Studies*, 58, 82–92.

Moreno-Delafuente, A., Garzo, E., Moreno, A., & Fereres, A. (2013). A plant virus manipulates the behavior of its whitefly vector to enhance its transmission efficiency and spread. *PLoS One*, 8(4), e61543.

Mwebaze, P., Macfadyen, S., De Barro, P., Bua, A., Kalyebi, A., Bayiyana, I., Tairo, F., & Colvin, J. (2024). Adoption determinants of improved cassava varieties and intercropping among East and Central African smallholder farmers. *Journal of Agricultural and Applied Economics Association*, 3(2), 292–310.

Reincke, K., Vilvert, E., Fasse, A., Graef, F., Sieber, S., & Lana, M. A. (2018). Key factors influencing food security of smallholder farmers in Tanzania and the role of cassava as a strategic crop. *Food Security*, 10, 911–924.

Stan Development Team. (2022). RStan: The R interface to Stan, R package version 2.21.7. In R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://mc-stan.org/

Tankam Chedjou, I., Donnelly, R., & Gilligan, C. A. (2025). Optimizing crop varietal mixtures for viral disease management: A case study on cassava virus epidemics. *PLoS Computational Biology*, 21(9), e1012842.

World Trade Organization. (1995). *The WTO agreement on the application of sanitary and phytosanitary measures (SPS agreement)*. World Trade Organization.

## SUPPORTING INFORMATION

Additional supporting information can be found online in the Supporting Information section at the end of this article.
<figure><img src="image_13.png" /></figure>

**Supporting Information** S1. The *estimate_virus_parameters_PT* function.

**Supporting Information** S2. The *estimate_virus_parameters_SPT* function.

**Supporting Information** S3. The *calculate_epidemicProbability* function.

**Supporting Information** S4. The *AP_data_simulator* function.

**Supporting Information** S5. Validation of virus parameter estimates.

**Supporting Information** S6. AP dataset structure in the *EpiPvr* package.

**How to cite this article:** Donnelly, R., Tankam Chedjou, I., & Gilligan, C. A. (2025). Plant pathogen profiling with the EpiPvr package. *Methods in Ecology and Evolution, 00*, 1–13.
https://doi.org/10.1111/2041-210x.70219