The authors have declared that no competing interests exist.
Current address: Faculty of Veterinary Medicine, Utrecht University, TD Utrecht, The Netherlands
To evaluate and compare the risk of emerging vector-borne diseases (VBDs), a Model for INTegrated RISK assessment, MINTRISK, was developed to assess the introduction risk of VBDs for new regions in an objective, transparent and repeatable manner. MINTRISK is a web-based calculation tool, that provides semi-quantitative risk scores that can be used for prioritization purposes. Input into MINTRISK is entered by answering questions regarding entry, transmission, establishment, spread, persistence and impact of a selected VBD. Answers can be chosen from qualitative answer categories with accompanying quantitative explanation to ensure consistent answering. The quantitative information is subsequently used as input for the model calculations to estimate the risk for each individual step in the model and for the summarizing output values (rate of introduction; epidemic size; overall risk). The risk assessor can indicate his uncertainty on each answer, and this is accounted for by Monte Carlo simulation. MINTRISK was used to assess the risk of four VBDs (African horse sickness, epizootic haemorrhagic disease, Rift Valley fever, and West Nile fever) for the Netherlands with the aim to prioritise these diseases for preparedness. Results indicated that the overall risk estimate was very high for all evaluated diseases but epizootic haemorrhagic disease. Uncertainty intervals were, however, wide limiting the options for ranking of the diseases. Risk profiles of the VBDs differed. Whereas all diseases were estimated to have a very high economic impact once introduced, the estimated introduction rates differed from low for Rift Valley fever and epizootic haemorrhagic disease to moderate for African horse sickness and very high for West Nile fever. Entry of infected mosquitoes on board of aircraft was deemed the most likely route of introduction for West Nile fever into the Netherlands, followed by entry of infected migratory birds.
International trade, globalization, and changes in demographics, land use, and climate all contribute to the geographical expansion of vector-borne diseases (VBDs), not only threatening public health but also livestock health. In the last decades, the Netherlands experienced two major epidemics of VBDs affecting ruminants resulting in severe economic losses for the Dutch livestock industry, namely bluetongue in 2006–2007 and Schmallenberg in 2011–2012 [
A Framework to assess Emerging VEctor-borne disease Risks (FEVER) was developed that addresses all elements that contribute to the risk of vector-borne animal diseases for newly affected areas, i.e. the probabilities and consequences of entry, establishment, spread and persistence [
Available methods to combine the separate elements of a risk assessment into a summarising output parameter range from relatively simple methods such as risk matrices [
In this paper we describe the resulting calculation tool MINTRISK (Model for INTegrated RISK assessment) and illustrate its application in a risk assessment for emerging VBDs. We used MINTRISK to assess the risk of four VBDs for the Netherlands with the aim to prioritise diseases for preparedness. The outcome of this risk assessment can be used to support policy makers in managing the risk of VBD introduction.
MINTRISK is a semi-quantitative calculation tool based on the FEVER framework [
MINTRISK uses six steps to evaluate the introduction risk of vector-borne livestock diseases (
MINTRISK was built as a web-based tool that can be accessed at
Input into MINTRISK is entered by scoring a set of questions for each step, mostly by choosing from five qualitative answer categories (very low, low, moderate, high, very high) with accompanying quantitative explanation tailored for each question (
When performing the model calculations in MINTRISK, the qualitative answers to the questions are converted into numerical values between 0 and 1 using a linear scale. For each answer category, a most likely value has been set with an associated uncertainty interval as indicated in
The numerical values sampled from the triangular distributions are subsequently log-transformed to obtain quantitative input values for the model calculations (see
Calculations not only return a semi-quantitative risk score for each individual step in MINTRISK, but also for the summarizing output parameters, i.e. the rate of introduction, the epidemic size, and the overall risk estimate (
In the next paragraphs, calculations in MINTRISK are described in detail per step. A comprehensive overview of all parameters in MINTRISK is given in
Step | Parameter | Description | Type |
---|---|---|---|
Annual volume of host animals / vectors / commodities / humans moved along the pathway from the risk region to the area at risk | User input | ||
Probability that pathogen will survive in the pathway until arrival in the area at risk | User input | ||
Probability that pathogen is still present upon arrival in the area at risk despite preventive measures | User input | ||
Probability of pathway being infected if disease is endemic in the risk region (equals prevalence of infection in host animals / vectors / humans in endemic situation) | User input | ||
Probability of pathway being infected if disease is endemic in the risk region | Calculated | ||
Prevalence of infection in host animals / vectors / humans in the risk region if disease is epidemic | User input | ||
Frequency of epidemics (per year) in the risk region | User input | ||
Fraction of the total area of the risk region affected by an epidemic | User input | ||
Length of the high-risk period in years in the risk region | User input | ||
Annual rate of entry (number of infected entries per pathway) | Calculated | ||
Basic reproduction number of the infection in a fully susceptible population with abundant presence of vectors | User input | ||
Fraction of the host population susceptible to infection in the area at risk | User input | ||
Reduction factor to account for non-homogeneous distribution of the vector in the area at risk | User input | ||
Reproduction number of the infection under optimal conditions in the area at risk | Calculated | ||
Semi-quantitative risk score for the optimal reproduction number ( |
Risk score | ||
Probability of first transmission step (from host to vector or from vector to host) | User input | ||
Probability of second transmission step (from vector to host or from host to vector) | User input | ||
Probability of establishment in the area at risk | Calculated | ||
Effective reproduction number accounting for the dilution effect | Calculated | ||
Effective reproduction number when control measures are applied | Calculated | ||
Dilution effect due to presence of non-susceptible hosts in the area at risk | User input | ||
Effectiveness of vector control measures in reducing spread of the infection | User input | ||
Effectiveness of other control measures aimed at host animals in reducing spread of the infection | User input | ||
Number of infection generations in one vector season | User input | ||
Effective number of infection generations in one vector season, considering spatial and ecological conditions limiting spread of infection | Calculated | ||
Number of infection generations until detection of disease | Calculated | ||
Overlap between vector abundance and susceptible host animal density | User input | ||
Inhibition of local spread by spatial effects | User input | ||
Contribution of vectors to long-distance spread | User input | ||
Contribution of host animals to long-distance spread | User input | ||
Length of the high-risk period in the area at risk (years) | User input | ||
Length of the vector season (fraction of the year) | User input | ||
Population size of susceptible host animals (epidemiological units) in the area at risk | User input | ||
Total number of infected hosts (epidemiological units) during the first vector season | Calculated | ||
Number of infected hosts (epidemiological units) of the last infection generation of the vector season (before start of the adverse/winter season) | Calculated | ||
Probability that the infection overwinters until the next vector season per infected host animal present at the end of the vector season | Calculated | ||
Expected number of infected hosts (epidemiological units) at the start of the next vector season | Calculated | ||
Direct agricultural losses per infected epidemiological unit | User input | ||
Indirect agricultural losses per infected epidemiological unit | User input | ||
Economic losses due to human cases per 100 infected epidemiological units (only if zoonotic) | User input | ||
Indirect agricultural losses for the entire area at risk | User input | ||
Economic losses due to side effects for the entire area at risk | User input | ||
Economic impact (Euros) | Calculated | ||
Semi-quantitative risk score for the economic impact ( |
Risk score | ||
Socio-ethical impact | Calculated | ||
Environmental impact | Calculated | ||
Annual number of entries resulting in successful establishment in the area at risk | Calculated | ||
Semi-quantitative risk score for the rate of introduction ( |
Risk score | ||
Final semi-quantitative risk score for the rate of introduction | Risk score | ||
Estimated epidemic size (total number of infected epidemiological units over 4 vector seasons) | Calculated | ||
Overall risk estimate (expected annual economic loss due to introduction of the infection) | Risk score |
There is usually not one single route along which a pathogen can enter a new area. Therefore, MINTRISK allows the risk assessor to assess the rate of entry via different pathways and from different risk regions. Pathways can either be classified as infected host animals or their products (
The annual rate of entry (
MINTRISK thus offers the risk assessor the option to indicate whether the VBD is endemic or epidemic in the risk region, where epidemic was defined as the incidental presence of the disease in regions where the disease is normally absent, and endemic was defined as the continuous presence of the disease in the risk region over a longer period (i.e. years). The risk assessor can also decide to enter values for both epidemic and endemic presence. In that case, MINTRISK uses a worst-case approach by selecting the parameter (
The probability of transmission is estimated in an early stage in MINTRISK, as there is no need for a full risk assessment if transmission is very low or even negligible. The risk score for the probability of transmission is an indication of the reproduction number
The probability of establishment depends on the pathway, local area, and time of entry of the infection in the area at risk and is thus calculated separately for each pathway. For a successful establishment, the infection needs to complete a full transmission cycle, i.e. the infection has to pass from an introduced infected host animal via a local vector to an indigenous host animal, or from an introduced infected vector via an indigenous host animal to a local vector. The probability of establishment (
The rate of introduction, i.e. the expected annual number of entries resulting in successful establishment, is calculated separately for each pathway that is entered into MINTRISK by the risk assessor, using the output parameters of the steps for entry and establishment. The rate of introduction (
To define the qualitative risk level for the rate of introduction, also the output of the transmission step is considered, but only when
Then, the risk assessor is asked to select a maximum of three pathways to include in the further MINTRISK calculations, which naturally are those pathways that have the highest value for
In this step, the extent of spread of the disease in the first vector season is evaluated. The epidemiological unit considered in this step should equal the epidemiological unit for which the basic reproduction number was given in the transmission step (see paragraph on Transmission) and can, e.g., be individual host animals or herds or flocks. The semi-quantitative risk score for spread is based on the total number of epidemiological units that is expected to get infected during the first vector season. This is calculated primarily from the optimal reproduction number (
Spatial characteristics can either limit or favour spread of the infection. In MINTRISK, four parameters are considered, i.e. the overlap between vector abundance and host animal density in the area at risk (
Please note that the questions to assess
As soon as the infection is detected, control measures will be implemented that reduce the transmission of the infection, resulting in a lowered value of the reproduction number (
The rate of transmission of the infection is thus expected to differ between the first phase of the epidemic, which is the high-risk period with undetected spread of the infection and no control measures in place yet, and the second phase of the epidemic that starts upon detection of the disease resulting in control measures. In MINTRISK, the length of the first phase is expressed by the number of infection generations until detection of the disease (
In calculating the total number of epidemiological units infected during the first vector season (
MINTRISK also calculates the total number of infections of the last infection generation in the vector season (
To estimate the likelihood of persistence, the probability that the infection can survive into the next vector season is evaluated. To this end, the probability of overwintering in both the host animal and vector population are addressed, as well as the probability of overwintering via other mechanisms such as non-zero vector activity [
The expected epidemic size (
In calculating the epidemic size, we assumed that the spread of the infection in the first vector season started with a single infected epidemiological unit, and that each infected epidemiological unit at the start of the new vector season will have a similar probability of inducing new infections. If persistence is high, this might result in a large number of infections in the next vector seasons, exceeding the total population size. Therefore, the total population size (
The impact assessment consists of the evaluation of the economic, socio-ethical and environmental consequences related to the introduction and spread of a vector-borne disease. While the economic consequences can be expressed in monetary values, the quantification of socio-ethical and environmental consequences is less straightforward. To avoid the subjective translation of these elements into monetary or utility values, MINTRISK only accounts for the economic consequences in the overall risk estimate. The questions on socio-ethical and environmental consequences were nevertheless included to raise awareness. Results of these sections can be used to indicate the potentially adverse consequences of disease introduction even if economic consequences are limited.
The main variables determining the impact of disease introduction are the number of epidemiological units (host animals and/or farms) infected, the geographical area affected by the disease, the control measures applied to contain or eradicate the pathogen, and–in case the disease is zoonotic–the number of humans infected and the severity of illness. The economic impact (
No accompanying quantitative explanation is available when entering the answer categories for the questions in the sections on socio-ethical and environmental impact in MINTRISK. In these sections of MINTRISK, the numerical values sampled are therefore not log-transformed, but directly used to calculate the semi-quantitative risk scores and resulting qualitative risk levels. The socio-ethical impact (
The overall risk estimate in MINTRISK provides an indication of the expected annual economic loss due to introduction of the vector-borne disease. This parameter is only calculated at the level of semi-quantitative risk scores. Usually, risk is calculated as the product of probability and impact. Since the risk scores are on a log10 scale, in this model the risk score for the rate of introduction (
We evaluated the annual introduction risk of four VBDs for the Netherlands. Diseases included were OIE listed [
Disease | Pathogen |
Vertebrate host animal | Vector | Zoonosis | Geographical distribution |
---|---|---|---|---|---|
AHS virus ( |
Equines | No | Sub Saharan Africa | ||
EHD virus serotype 6 ( |
Deer, bovines, sheep | No | Morocco, Algeria, Tunisia, Turkey, Reunion Island, Guadeloupe, Australia, USA | ||
RVF virus ( |
Bovines, sheep, goats, wild ungulates, rodents | Yes | Africa, Arabian Peninsula | ||
West Nile virus ( |
Birds, equines | Yes | South, Central and Eastern Europe, Middle East, Asia, Australia, North, Central and South America, Africa |
a Sources
African horse sickness [
Epizootic haemorrhagic disease [
Rift Valley fever [
West Nile fever [
b Genus and family of pathogen given between brackets.
The risk assessment was an update of an assessment performed in 2015 [
Type | Pathway | AHS | EHD | RVF | WNF |
---|---|---|---|---|---|
Legal trade in livestock/equines | X | ||||
Illegal trade in livestock/equines | |||||
Import of zoo animals | X | ||||
Movement of competition horses | X | ||||
Migratory birds | |||||
Biological material including modified live vaccines | X | X | |||
Transport vehicles (aircraft, ship, road transport) | |||||
Containers on aircraft or ship | |||||
Imported products (plant material, tires) | X | ||||
Traded animals (livestock, pets) | |||||
Migration of wildlife | |||||
Migratory birds |
Pathways selected for inclusion in the model calculations are indicated in bold.
Questions in MINTRISK were answered using information from global databases, scientific literature, and expert opinion. Answers to all questions in the assessment are documented in
The rate of introduction provides an indication of the annual probability of successful entry, i.e. entry of the pathogen resulting in establishment in the area at risk and subsequent spread. The estimated rate of introduction for the pathway contributing most to the introduction risk varied from low for EHD (median risk score 0.32) and RVF (median risk score 0.39) to very high (median risk score 0.87) for WNF (
AHS = African horse sickness; EHD = epizootic haemorrhagic disease; RVF = Rift Valley fever; WNF = West Nile fever.
For both AHS and EHD we assumed that at least one of the Palearctic
The impact of a VBD is largely dependent on the epidemic size (
AHS = African horse sickness; EHD = epizootic haemorrhagic disease; RVF = Rift Valley fever; WNF = West Nile fever.
The estimated economic impact was very high for all four diseases (
AHS = African horse sickness; EHD = epizootic haemorrhagic disease; RVF = Rift Valley fever; WNF = West Nile fever.
Socio-ethical impact is expected to be high to very high for all diseases except EHD (
The overall risk estimate calculated by MINTRISK takes into account the results of all six steps of the FEVER framework. The resulting risk scores can be used to prioritize VBDs for risk management. The overall risk estimate was very high for all evaluated VBDs but EHD (
AHS = African horse sickness; EHD = epizootic haemorrhagic disease; RVF = Rift Valley fever; WNF = West Nile fever.
Dots indicate the median values for each disease with the lines enclosing the 95% uncertainty interval. Values outside the dotted square (i.e. beyond 0 and 1) indicate an extremely low (<0) or an extremely high (>1) risk. AHS = African horse sickness; EHD = epizootic haemorrhagic disease; RVF = Rift Valley fever; WNF = West Nile fever.
Results indicate that the four VBDs considered in this study mainly differed for their rate of introduction and less for the expected economic impact of disease. Nevertheless, it is important to not only consider the probability of introduction when performing an import risk assessment, but also the impact of disease. This is even more true for VBDs if there is no competent vector or host in the area where the entry occurs or when weather conditions are not favourable for establishment. Based on the results of this study, WNF should be prioritized for risk management in the Netherlands, both having the highest rate of introduction (
In a risk assessment by EFSA for the European Union (EU), WNF also had a much higher rate of introduction than AHS, EHD and RVF. However, results obtained by EFSA indicated a lower introduction risk with a moderate rate of introduction for WNF and a very low rate of introduction for AHS, EHD and RVF [
The risk of RVF introduction into the EU has recently been updated by EFSA [
The introduction risk of VBDs for European countries was also evaluated with bespoke models. De Vos et al. [
In contrast to the other VBDs, the main reservoir hosts for West Nile virus are wild birds rather than domestic animals. Evaluation of the introduction and transmission risk of WNF might therefore need additional parameters that were not included in MINTRISK. In MINTRISK, the probability of transmission and establishment is primarily based on the distribution of reservoir hosts and vectors, whereas environmental and climatic factors might be as important in evaluating these steps. Tran et al. [
MINTRISK is classified as a generic risk assessment tool that can be used to assess the introduction risk for multiple diseases, allowing for prioritization of diseases for risk management [
MINTRISK is one of the most complete generic risk assessment tools for disease introductions, not only addressing the probabilities of entry and exposure, but also the impact of disease over a longer period. Most generic tools in the veterinary field evaluate the introduction risk up till entry [
MINTRISK is a very flexible tool that can be used for both quick and in-depth risk assessments of VBDs. For a quick assessment, the questions can be answered by experts and a first indication of the risk can be obtained as soon as all questions have been answered, regardless of the level of uncertainty entered and the number of questions answered as ‘unknown’. An in-depth risk assessment, on the contrary, requires analysis of data derived from global databases on, e.g., worldwide disease occurrence and international trade, extensive literature search to estimate disease-related parameters, and expert consultation to complement any missing values. An in-depth risk assessment will in general result in narrowing of the uncertainty intervals, although uncertainty intervals can still be wide, as seen in the current assessment. This is due to the inherent nature of MINTRISK working with quantitative estimates sampled from intervals on a logarithmic scale rather than with exact values, although the tool allows the user to enter an ‘own value’ if an exact number is known. Even if ‘low’ uncertainty would be entered for all questions, i.e., input values are sampled in a range of 1 log10 difference only, the uncertainty sampled for the individual input parameters of the model will add to a relatively high uncertainty for the overall risk estimate. The logarithmic scale used to answer questions in MINTRISK was chosen to account for the fact that values of many input parameters needed for vector-borne risk assessment are not exactly known, although the order of magnitude is often available. The logarithmic scale allows the risk assessor to provide a rather robust estimate for these parameters rather than pretending a false sense of preciseness by entering an exact value. As a consequence, results of MINTRISK are primarily useful to compare VBDs or areas at risk for their introduction risk, rather than providing an exact estimate of the introduction risk. A similar approach was used by Havelaar et al. [
Not all questions are equally important in assessing the risk of VBDs using MINTRISK. Therefore, the risk assessor is advised to put most efforts to answer those questions that have most impact on the risk estimate. Examples would be the prevalence of disease in vectors and hosts in the risk regions; the number of vectors, hosts or commodities transported to the area at risk; the reproduction number R0; and the number of infection generations in a single vector season. Although the questions on overwintering also affect the overall risk estimate, the effort on answering those questions could be limited to the most likely overwintering route as only this answer will be used to estimate the likelihood of persistence (
MINTRISK was developed to enable comparison of VBDs and/or areas with respect to their introduction risk in an objective, transparent and repeatable manner. This was achieved by providing quantitative explanations for the qualitative answer categories in the tool. There are, however, a few questions in MINTRISK that are very hard to quantify, and these come without accompanying quantitative explanation. These include, e.g., questions on the socio-ethical and environmental impact of VBDs. The resulting risk scores for socio-ethical and environmental impact were therefore not used to calculate the overall risk estimate. They are, however, presented as separate output to raise awareness on possible adverse effects even if economic impact would be limited.
Pathways in MINTRISK have not been predefined to allow for flexibility accounting for the many different modes in which VBDs might be transported from risk regions to the area at risk. The risk assessor can enter multiple pathways, but only a maximum of three are used to calculate the overall risk estimate. In developing the tool, we assumed that there are only few pathways that drive the risk, especially when calculations are performed on a logarithmic scale. The risk assessor needs to select the pathways to include in the risk calculations based on the individual pathway’s risk score for the rate of introduction (
MINTRISK is a flexible tool to assess the introduction risk of VBDs in an objective, transparent and repeatable manner. The tool provides semi-quantitative risk scores that can be used for prioritization purposes. The overall risk estimate is calculated from the rate of introduction and the economic impact of disease. Results of a case study estimating the risk of four VBDs for the Netherlands indicated that the overall risk estimate was comparable for all diseases, despite the diseases having a different risk profile. Visualisation of the risk scores in a risk profile diagram allows for interpretation of these risk profiles. All diseases were estimated to have a high economic impact once introduced, but the estimated introduction rates differed, with WNF being the disease most likely to be introduced. Shortly after finishing this study, WNF was detected indeed in the Netherlands in both wild birds and humans.
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The authors would like to thank Barbara van der Hout (Wageningen Economic Research) for technical assistance in the development of MINTRISK.
PONE-D-21-20706
Assessing the introduction risk of vector-borne animal diseases for the Netherlands using MINTRISK: A Model for INTegrated RISK assessment
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“The development of MINTRISK was funded by the Dutch Ministry of Agriculture, Nature and Food Quality (KB-12-009.01-001), Wageningen University & Research (KB-33-001-006-WBVR) and the European Food Safety Authority (NP/EFSA/ALPHA/2016/13-CT01; NP/EFSA/ALPHA/2017/10; PO/ALPHA/2019/06). The case study on vector-borne diseases was funded by the Dutch Ministry of Agriculture, Nature and Food Quality (BO-20-009-026). The authors would like to thank Barbara van der Hout (Wageningen Economic Research) for technical assistance in the development of MINTRISK.”
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Reviewer #1: Over the years I have been following the development of the MINTRISK model and therefore, I read, with great interest, the manuscript, especially as it described the application of the MINTRISK model to assess the risk of introduction of specific vector borne diseases (VBDs: AHS, EHD, RVF, WNF) for a specific country (the Netherlands).
MINTRISK model includes (almost) all major parameters of the ecology and epidemiology for wide variety of VBDs transmitted by Dipteran vectors in a comprehensive way for others to appreciate; tick borne diseases fall outside its scope as described in 576-583 (since this is not a discovery of the current paper I think the authors could present MINT RISK as such from the beginning). The fact that it takes 98 specific questions to be answered in a semi-qualitative manner for each pathways to assess the risk of a specific VBD for a specific area at a certain moment is a perfect illustration of the complexity and temporal and spatial variation of VBDs. Illustrating this is for me the most important merit of the MINTRISK project and model; it forms a great help to others, just starting in the field of VBDs modeling and risk assessing, that in depth knowledge of the disease system is required to even begin to understand, assess or to project risks of VBDs in context.
The down side is that applying the model to compare risk of VBDs in your own specific situation is very labor and data intensive, while the utility of the overall risk output, besides the appreciation of the complexity is not so obvious. The authors are well aware and focus mainly on discussing the individual scores which are much more informative than overall risk output. Complements for the authors to dare to write such an elaborate manuscript and to choose not to make short cuts.
It is obvious (e.g. lines 46-47, 364, 370-371, 486, 558, 569-570) that the MINTRISK model has been developed, applied to and described for the Netherlands before West Nile virus was introduced in the Netherlands in 2020. The authors attempted to address this fact in the text (lines 495-503) but, in my opinion, this could be improved. In addition, I would to invite the authors to embrace this situation and fully address this fact and how the results are in line or what surprised them or not. This is the test case and it would be a pity to let this go to waste. I am very interested in their take on the assessment of the high economic impact (due to estimated epidemic size) of WNV in the Netherlands while there are no currently no signs for this. Maybe they can elaborate why the estimate was so high while in reality this does not seem to pan out yet.
In the following I will elaborate on more specific points in detail.
Content:
Line 36: When addressing the main result (WNV has high introduction rate) in the abstract, as a reader, I would also like to find the main factor(s) causing this in the abstract. In addition I do not think it is surprising that all four VBDs chosen for this review have high economic impact, as this was probably one of the reason why they were chosen to be included. It would have been very interesting to choose another disease and see whether the gut feeling of importance diseases for Europe also was reflected in the MintRisk tool, e.g. JEV or EEE.
Line 46-47: As stated in the general comments above it is obvious that the manuscript has been written for the larger part before the introduction of WNV in the Netherlands. Please update the manuscript (see general comment above), including adapting this sentence by adding WNF to this list of recent introduced VBDs in the Netherlands.
Line 183 & 189: The reasoning, behind the definition of an area being patchy (<5%) and homogeneous (>5%) and the accompanying value setting of Dvector, escapes me. An explanation or example of both would help me understand.
Line 240: Please replace non-susceptible animal with non-susceptible host (there are many animals in an area). In my opinion this was scored good (looking in specific in WNV)
Line 249-254: Please add a comment on the anticipated resolution of this assessment of overlap.
Line 516-518: Please add reference on the EFSA report on Assessment of the introduction RVF by vectors into Europe Van Bortel et al. 2020.
Line 541: Strange statement. Why would one only consider USA as region of origin of WNV?
Line 562-566: The reasoning is original and a interesting source for the estimate, but the spill over from the virus amplification cycle in birds-mosquitoes to horses and humans is largely determined by exposure (and sampling and reporting bias) rather than a mathematical algorithm.
Line 569-570: Could you put this outcome in perspective with the current situation in areas where WNV is introduced. There is a large difference the evolution of WNV after introductions between countries (compare Spain and France with Italy and Greece, and how does situation of Germany fit in).
Figure 1: Although it is unlikely to change, the term Rate of Introduction in the text means something else than what I would “intuitional” would think ( I would think it is synonym for entry). However the terminology is applied according the description of the paper so it is ok.
Figure 3 fig 5: Since the model is best used when comparing diseases I would put arrange the x-axes by parameter and not disease. The authors could also consider whether it is feasible and necessary to subject the data to statistical analysis whether the various risk scores are actually different between diseases.
Table 2: Why is importation of zoo-animals not considered as a source of WNV
Editorial:
Line 30: In line with the other three diseases I would refer to West Nile fever (WNF) as the disease caused by the infection with West Nile virus (and not only West Nile). Please adapt this through the manuscript.
Line 113-114: Please are write the sentence that it becomes clear that ‘very low’ and ‘very high’ are names of the categories of the answers. The current sentence is now rather confusing. In addition, make sure you unify the term; both ‘answering categories’ and ‘answer categories’ (e.g. in line 123) are used.
Line 132: Sentence is missing a word or do calculations return a risk score
Line 371: Please adjust reference numbers. Should be [11-12]. As I did not check all references, please recheck all references in the list and numbering in the text.
Table 1: I cannot find a definition of host in the text. Please add as the one of line 140 does not suffice to exclude humans, who I as a biologist define as a vertebrate animal (I do not think anthropogenic definitions helps us to understand transmission cycles).
S1-S3; The questions start with Q18. Where are Questions 1-17?
S3: I think the tables are a bit confusing as the columns of pathways are still present when they are only considered until question 51. Please adjust.
Reviewer #2: The authors present a tool called MINTRISK, which can be used to assess the risk of introduction and spread of vector-borne diseases in new areas outwith their current range. The model not only considers the potential for outbreaks in the area of concern but also the potential economic and (to a slightly lesser extent) societal impacts of vector-borne disease outbreaks if they were to occur. Applications to four diseases (African horse sickness, epizootic haemorrhagic disease Rift Valley fever and West Nile) and the risks they pose to the Netherlands are given. The work appears to be novel, extending on the previously established FEVER framework to combine different aspects of risk assessments in an objective way. There were some places where I struggled to follow how the model was constructed and so I have some suggestions for areas of clarification and/or improvement. However, whilst it may look like quite a lot of comments, I would hope that these are relatively minor and are mainly just points of clarification, as I think that this is a valuable tool which should be published.
Details of suggested amendments are given below. I have tried to score then by importance with *** denoting the most important and * denoting a fairly incidental comment in the hope that that is of help to the authors:
Introduction (Paragraph 1) (**): It seems odd to be that there is no mention that West Nile has recently been reported in the Netherlands. I notice this is mentioned in the discussion but in particular lines 46-50 don’t read right to me knowing that WN has recently been found in the Netherlands.
Material and Methods (***): There are a lot of parameter values in this paper. A table detailing what each parameter is called, what it measures, where it comes from (user input or otherwise) would be very helpful as I was constantly scrolling up and down to remember what each thing was and how it was calculated. I did realise (too late) that there is a table of questions etc. in Appendix 1 which goes some way towards doing this but what I would really like to see is a Table in the main text with all there terms involved in the Equations included.
Lines 86-88 (**): When I first read through this I was somewhat confused because it seems that transmission is simply the first half of establishment e.g. transmission is the ability of the pathogen to spread to vector to host and establishment is the ability of the pathogen to spread from vector to host and back again. This made me worry that the same thing may be modelled twice. I see now that it seems to be more of an either/or approach where the introduction risk score comes from entry*establishment if Ropt>1 but it can come from just the transmission term (Ropt) if Ropt<1. I wonder if this can be clarified earlier on to avoid my initial concerns/confusion?
Line 162 (**): “frequency of epidemics per year”?
Eqn 2 (***): This is stated to be a probability but it is not possible that it could be >1? For example, Fepi=0.5, Area=1, HRPrr=3, Prev=0.7 gives Pepi_pt=1.05. Whilst this may be an unlikely set of values I don't see why it would be implausible?
Lines 188-189 (*): The specific question in MINTRISK is "What is the estimated value of the basic reproduction ratio?", which is less descriptive than the definition here. I think it would be preferable to use this description in the MINTRISK question, as R will be affected by vector-host ratio so it may be important for users to know the assumed context?
Lines 189-191 (**): A 10% reduction in R0 when the vector is present in less than 5% of the area doesn't seem like very much - and likewise no reduction for anything more than 5% coverage doesn't seem like much. What's the justification for this choice? How sensitive are results to it?
Line 203 (*): Intuitively when I think of “introduction” I find myself thinking of what has been described in the paper as “entry”. I think it would be easier to follow if this section talked about “introduction and establishment” (although I appreciate that doesn’t fit nicely into the equations).
Lines 227-230 (**): So am I correct that each individual result is based on only a single pathway (though that pathway could be one of up to three for any given run)? Could this not lead to quite severe underestimation of risk i.e. if there are three pathways all with similar risk will that not lead to a higher overall risk (because it would be cumulative) than the case where there is one pathway with this risk and the other two have negligible risk?
Lines 254-255 (**): I don't follow this logic. Why would long-distance spread of hosts or vectors mean more infection generations in a season (or vice versa). I can see how it would increase the population at risk but not the generation time. To be clear, when I read infection generation I'm essentially thinking of the length of a gonotrophic cycle - is that the definition here? I think there needs to be a bit more justification/clarification here.
Lines 255 (**): Why 50%? How sensitive are the results to this choice?
Eqn 11 (***): This just seems to add the value for Reff in each generation but surely the number of infectives at the start of the generation will need to be included each time. For example, if Reff was 5, there were 3 generations and HRP>Tseason then this formula would give 15 infections (I think); however surely it should be 5 new infections in generation 1, then each of those 5 infections produces 5 more in generation 2 (giving 5*5=25 new infections) and then those 25 each generate a 5 new infections (giving 25*5=125 new infections). So the total number of infections would be 5+25+125=155? Unless the idea is that it is a separate introduction for each generation season and so there are 5 secondary infections each generation season with no onward spread from there but then I think the text needs to be much clearer about is meant by spread because I would interpret that is repeated introductions rather than spread. What am I missing here? Note my same confusion reappears in Eqns 12 and 14.
Lines 295-297 (**): What is the justification for focussing only on the most likely route? If there were multiple potential routes it should be possible to determine the expected number of cases of overwintering by at least one of those routes. I think what's done is fine when it's expected that one route will dominate but if there are multiple likely routes then will it not underestimate risk?
Eqn 14 (**): I think I read somewhere that you only consider the first 3 years, which would explain the 3 at the top of the last summation, however I can’t find where I read that now. It would be good to mention that next to this equation. If I didn’t read that somewhere, why only sum to 3?
Line 336 (**): Any justification for the choice of 100?
Line 346-351 (**): Why the maximum? I would have thought a cumulative measure of socio-ethical impact would be preferable in cases where one impact doesn’t dominate?
Lines 434-439 (**): Does this not suggest an issue with the model calibration if a high number of infections is able to drive a high economic impact even when the user has specified economic impact to be low? Has there been much evidence of economic impact in worse affected areas like Italy, for example?
Appendixes (**): Some explanation of the parameterisation of the log transformation would probably be helpful i.e. it is of the form a^((IV+b)*c) but it would be good to explain briefly how a, b and c are determined. Likewise for Appendix 2.
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“The development of MINTRISK was funded by the Dutch Ministry of Agriculture, Nature and Food Quality (KB-12-009.01-001), Wageningen University & Research (KB-33-001-006-WBVR) and the European Food Safety Authority (NP/EFSA/ALPHA/2016/13-CT01; NP/EFSA/ALPHA/2017/10; PO/ALPHA/2019/06). The case study on vector-borne diseases was funded by the Dutch Ministry of Agriculture, Nature and Food Quality (BO-20-009-026). The authors would like to thank Barbara van der Hout (Wageningen Economic Research) for technical assistance in the development of MINTRISK.”
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The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.”
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We have removed the information on funding from the Acknowledgements Section. The Funding Statement should read as follows:
“The development of MINTRISK was funded by the Dutch Ministry of Agriculture, Nature and Food Quality (KB-12-009.01-001), Wageningen University & Research (KB-33-001-006-WBVR) and the European Food Safety Authority (NP/EFSA/ALPHA/2016/13-CT01; NP/EFSA/ALPHA/2017/10; PO/ALPHA/2019/06). The case study on vector-borne diseases was funded by the Dutch Ministry of Agriculture, Nature and Food Quality (BO-20-009-026).”
3.Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.
We reviewed the reference list and ensured that it is complete and correct.
Changes made to the reference list are:
• Two references (Sikkema et al., 2020 and Vlaskmap et al., 2020) were moved up in the reference list, because these publications are now referred to in the Introduction section.
• One new reference (Van Bortel et al., 2020) was added to the reference list as suggested by reviewer 1.
• Five new references (Nasci et al., 2001; Braks et al., 2017; Vogels et al., 2017; Rudolf et al., 2017; ECDC website) were added because of extending the discussion of the results for WNF.
Furthermore, a reference on the FEVER framework (De Vos et al., 2011) and two references on survival of WNV in mosquitoes during winter (Nasci et al., 2001; Rudolf et al., 2017) were added to S3 Appendix.
Response to Reviewers
We would like to thank both reviewers for carefully reading the manuscript and the useful comments they made. We appreciate the efforts they took to really understand how MINTRISK has been built. We have considered all comments made by the reviewers and revised the manuscript accordingly.
Please note that reference is made to line numbers in the marked-up copy of the manuscript highlighting changes made to the original version (‘Revised Manuscript with Track Changes’).
Reviewer #1
Over the years I have been following the development of the MINTRISK model and therefore, I read, with great interest, the manuscript, especially as it described the application of the MINTRISK model to assess the risk of introduction of specific vector borne diseases (VBDs: AHS, EHD, RVF, WNF) for a specific country (the Netherlands).
We would like to thank this reviewer for carefully reading the manuscript and providing helpful comments, with a special focus on West Nile fever. We have considered all comments given by this reviewer and revised the manuscript accordingly.
MINTRISK model includes (almost) all major parameters of the ecology and epidemiology for wide variety of VBDs transmitted by Dipteran vectors in a comprehensive way for others to appreciate; tick borne diseases fall outside its scope as described in 576-583 (since this is not a discovery of the current paper I think the authors could present MINT RISK as such from the beginning). The fact that it takes 98 specific questions to be answered in a semi-qualitative manner for each pathways to assess the risk of a specific VBD for a specific area at a certain moment is a perfect illustration of the complexity and temporal and spatial variation of VBDs. Illustrating this is for me the most important merit of the MINTRISK project and model; it forms a great help to others, just starting in the field of VBDs modelling and risk assessing, that in depth knowledge of the disease system is required to even begin to understand, assess or to project risks of VBDs in context.
When starting MINTRISK, the scope was risk assessment of livestock diseases transmitted by arthropod vectors including ticks. Although the tool has been most extensively used (and therefore tested) for Dipteran vectors, this is not to say that the tool could not be used for tick-borne diseases at all. The tool has also been used to assess the risk of two tick-borne diseases in the past (Crimean Congo haemorrhagic fever and babesiosis) (De Vos et al., 2016). Therefore we have not presented MINTRISK as a tool for Dipteran vectors from the beginning of the manuscript.
The down side is that applying the model to compare risk of VBDs in your own specific situation is very labor and data intensive, while the utility of the overall risk output, besides the appreciation of the complexity is not so obvious. The authors are well aware and focus mainly on discussing the individual scores which are much more informative than overall risk output. Complements for the authors to dare to write such an elaborate manuscript and to choose not to make short cuts.
It is obvious (e.g. lines 46-47, 364, 370-371, 486, 558, 569-570) that the MINTRISK model has been developed, applied to and described for the Netherlands before West Nile virus was introduced in the Netherlands in 2020. The authors attempted to address this fact in the text (lines 495-503) but, in my opinion, this could be improved. In addition, I would to invite the authors to embrace this situation and fully address this fact and how the results are in line or what surprised them or not. This is the test case and it would be a pity to let this go to waste. I am very interested in their take on the assessment of the high economic impact (due to estimated epidemic size) of WNV in the Netherlands while there are no currently no signs for this. Maybe they can elaborate why the estimate was so high while in reality this does not seem to pan out yet.
In the following I will elaborate on more specific points in detail.
In the Introduction we have now added West Nile fever as one of the vector-borne diseases that were recently introduced into the Netherlands. In the Discussion section, we have elaborated on the results of MINTRISK for WNF, especially the economic impact, and tried to explain why results in MINTRISK are different from the situation observed in the Netherlands so far. As stated in the Discussion section, the assessment of the economic impact in MINTRISK is less straightforward for West Nile fever, with the reservoir hosts being wild birds rather than livestock.
Content:
Line 36: When addressing the main result (WNV has high introduction rate) in the abstract, as a reader, I would also like to find the main factor(s) causing this in the abstract. In addition I do not think it is surprising that all four VBDs chosen for this review have high economic impact, as this was probably one of the reason why they were chosen to be included. It would have been very interesting to choose another disease and see whether the gut feeling of importance diseases for Europe also was reflected in the MintRisk tool, e.g. JEV or EEE.
We added information on the main introduction routes for WNF to the abstract. As this resulted in > 300 words, we removed the first introductory sentence of the abstract.
We agree with the reviewer that it would have been interesting to also apply MINTRISK to vector-borne diseases that are considered less of a threat for the Netherlands. The selection of vector-borne diseases was made in close cooperation with the main funder of this work, the Dutch Ministry of Agriculture, Nature and Food Quality and was based on a sense of urgency at the time the study was initiated. Adding a new disease to the current analysis is data and labour intensive indeed and was not considered essential for the current publication in which the case study is used to illustrate the application of MINTRISK.
Line 46-47: As stated in the general comments above it is obvious that the manuscript has been written for the larger part before the introduction of WNV in the Netherlands. Please update the manuscript (see general comment above), including adapting this sentence by adding WNF to this list of recent introduced VBDs in the Netherlands.
The reviewer is right that most of the manuscript was prepared before the introduction of WNV in 2020, resulting in omission of this recent incursion in the Introduction section. We have now added the introduction of West Nile in lines 48-50.
Line 183 & 189: The reasoning, behind the definition of an area being patchy (<5%) and homogeneous (>5%) and the accompanying value setting of Dvector, escapes me. An explanation or example of both would help me understand.
This reduction value was included to account for those conditions where a vector is definitely not abundantly present. However, relationships between vector abundancy and transmission are not easy to incorporate in a generic model, since transmission could still be very efficient in those areas where the vector is present indeed. If the vector is present in a few areas, the transmission will go smoothly in those few areas, while there will be very slow spatial transmission in larger areas. If R0 is sufficiently high, the spill-over from one infected sub-area will lead to spread to other subareas with vectors, but with substantial delay. If R0 is low, i.e. close to 1, there will be a lot of opportunity for fade out, even if there would be multiple epidemic starts. Thus, a limited reduction of R0 appears to be the best way to easily incorporate this aspect. This question and parameter were built into MINTRISK to raise awareness with the risk assessor that these elements have to be considered when estimating transmission of vector-borne diseases. Although Dvector will contribute to a proper estimate for the transmission rate, it cannot really account for the spatial and ecological differences within an area at risk that might either favour or hamper transmission.
We added an explanation on the parameterization of Dvector in lines 217-220.
Line 240: Please replace non-susceptible animal with non-susceptible host (there are many animals in an area). In my opinion this was scored good (looking in specific in WNV)
We changed animals into hosts. This was indeed scored correctly in MINTRISK, as we only accounted for animals that are hosts for the vectors to feed on.
Line 249-254: Please add a comment on the anticipated resolution of this assessment of overlap.
The spatial resolution to assess the overlap between vector abundance and host animal density is the area at risk considered in the risk assessment. This will often be quite a large geographical area, e.g. a country like in this study. We added “in the area at risk” to ensure that the reader is aware on which resolution the overlap is assessed.
Line 516-518: Please add reference on the EFSA report on Assessment of the introduction RVF by vectors into Europe Van Bortel et al. 2020.
We added a reference to Van Bortel et al., 2020 as suggested.
Line 541: Strange statement. Why would one only consider USA as region of origin of WNV?
This statement was made to contrast our results with those from Brown et al., 2012. They only considered commercial flights from the US, whereas we considered flights from all WN infected regions worldwide. We see no need to change our wording here.
Line 562-566: The reasoning is original and a interesting source for the estimate, but the spill over from the virus amplification cycle in birds-mosquitoes to horses and humans is largely determined by exposure (and sampling and reporting bias) rather than a mathematical algorithm.
We agree that spill over of WNV to humans or horses does not have a linear relationship with the number of infections in birds. As a result, the observed ratios that we used could be a huge overestimate (or underestimate) for the Dutch situation, partly explaining the unexpectedly high economic impact given for WNF. Since MINTRISK is a generic risk assessment tool for vector-borne diseases, that does not allow for all disease-specific details, we considered this the best way to estimate economic impact due to spill over.
Line 569-570: Could you put this outcome in perspective with the current situation in areas where WNV is introduced. There is a large difference the evolution of WNV after introductions between countries (compare Spain and France with Italy and Greece, and how does situation of Germany fit in).
In lines 622-634 we elaborate on the assumptions for transmission (R0 value) and overwintering that we used when performing the risk assessment for WNF in MINTRISK and discussed why these might not have been correct for the Dutch situation.
Figure 1: Although it is unlikely to change, the term Rate of Introduction in the text means something else than what I would “intuitional” would think ( I would think it is synonym for entry). However the terminology is applied according the description of the paper so it is ok.
We have added clear definitions for the three summarizing output parameters to the manuscript in lines 101-106, directly after the definitions of the individual steps as we realized that these were missing in the main text. Hopefully this helps to avoid confusion with the reader.
Figure 3 fig 5: Since the model is best used when comparing diseases I would put arrange the x-axes by parameter and not disease. The authors could also consider whether it is feasible and necessary to subject the data to statistical analysis whether the various risk scores are actually different between diseases.
We agree with the reviewer that the aim of the calculations is to compare over diseases. However, for Fig 3, we did not rearrange the x-axes, since this is impossible with different pathways evaluated for each disease. For Fig 5, we appreciated the suggestion and decided to rearrange the x-axis to ease the comparison between diseases.
A statistical analysis on simulated data is usually not very helpful. In theory, one can get any difference significant if sufficient iterations are run. Therefore, we compared the diseases based on their median risk scores and their uncertainty intervals.
Table 2: Why is importation of zoo-animals not considered as a source of WNV
Before starting the risk assessment in MINTRISK, a qualitative risk assessment was performed for each disease using the FEVER framework. FEVER provides an extensive list of potential pathways to consider including the import of exotic or zoo animals (De Vos et al., 2011). In this qualitative assessment we did consider importation of zoo animals as a source of WNV, but concluded that the probability of entry and establishment was very low. Two important differences with migratory birds are the low numbers involved and the fact that zoo birds are subjected to import regulations. Pathways with a very low probability for introduction in the qualitative assessment were not selected for the semi-quantitative assessment in MINTRISK.
Editorial:
Line 30: In line with the other three diseases I would refer to West Nile fever (WNF) as the disease caused by the infection with West Nile virus (and not only West Nile). Please adapt this through the manuscript.
Thanks for this useful suggestion. We have changed West Nile (WN) into West Nile fever (WNF) throughout the manuscript.
Line 113-114: Please are write the sentence that it becomes clear that ‘very low’ and ‘very high’ are names of the categories of the answers. The current sentence is now rather confusing. In addition, make sure you unify the term; both ‘answering categories’ and ‘answer categories’ (e.g. in line 123) are used.
We changed the order of the sentence in line 113-114 (now lines 129-131) and put the answer categories between quotes to avoid any confusion for the reader.
We thank the reviewer for spotting inconsistencies in our wording when indicating the answer categories. We changed answering categories into answer categories throughout the manuscript.
Line 132: Sentence is missing a word or do calculations return a risk score
Calculations do indeed return a semi-quantitative risk score for each individual step in MINTRISK as described in lines 142-144. We added semi-quantitative to line 132 (now line 149) and hope it is clearer now.
Line 371: Please adjust reference numbers. Should be [11-12]. As I did not check all references, please recheck all references in the list and numbering in the text.
Thanks for noticing. We adjusted the reference numbers here and checked all references and their numbering throughout the manuscript. No other mistakes were found. Please note that some reference numbers have been updated in the revised manuscript after inclusion of new references.
Table 1: I cannot find a definition of host in the text. Please add as the one of line 140 does not suffice to exclude humans, who I as a biologist define as a vertebrate animal (I do not think anthropogenic definitions helps us to understand transmission cycles).
We agree with the reviewer that vertebrate hosts would include humans if susceptible. We changed the caption and headings in Table 1 to make clear that we only indicate vertebrate host animals in the third column. Whether or not humans are a vertebrate host is indicated in the fifth column (zoonosis). We included a comment in the first paragraph of the Material and Methods (lines 87-89) to make explicit that MINTRISK models the infection dynamics between arthropod vectors and vertebrate host animals, and not humans.
S1-S3; The questions start with Q18. Where are Questions 1-17?
Numbering of questions in MINTRISK was aligned with numbering of questions in FEVER. The FEVER framework starts with a hazard identification, based on 17 questions. Since the questions of the hazard identification are not used to semi-quantitatively estimate the introduction risk, those questions are missing from MINTRISK. We added a footnote on this to S1 and S3 Appendices.
S3: I think the tables are a bit confusing as the columns of pathways are still present when they are only considered until question 51. Please adjust.
We have now split the tables into two: one for the first three steps in MINTRISK (entry, transmission and establishment) in which questions are answered by pathway, and one for the second three steps (spread, persistence and impact) in which answers to the questions are independent of pathways.
Reviewer #2
The authors present a tool called MINTRISK, which can be used to assess the risk of introduction and spread of vector-borne diseases in new areas outwith their current range. The model not only considers the potential for outbreaks in the area of concern but also the potential economic and (to a slightly lesser extent) societal impacts of vector-borne disease outbreaks if they were to occur. Applications to four diseases (African horse sickness, epizootic haemorrhagic disease Rift Valley fever and West Nile) and the risks they pose to the Netherlands are given. The work appears to be novel, extending on the previously established FEVER framework to combine different aspects of risk assessments in an objective way. There were some places where I struggled to follow how the model was constructed and so I have some suggestions for areas of clarification and/or improvement. However, whilst it may look like quite a lot of comments, I would hope that these are relatively minor and are mainly just points of clarification, as I think that this is a valuable tool which should be published.
We would like to thank this reviewer for carefully reading the manuscript and providing helpful comments. We appreciate the efforts made by this reviewer to fully understand the mathematical equations of MINTRISK. We have considered all comments made by the reviewer and revised the manuscript accordingly.
Details of suggested amendments are given below. I have tried to score then by importance with *** denoting the most important and * denoting a fairly incidental comment in the hope that that is of help to the authors:
Introduction (Paragraph 1) (**): It seems odd to be that there is no mention that West Nile has recently been reported in the Netherlands. I notice this is mentioned in the discussion but in particular lines 46-50 don’t read right to me knowing that WN has recently been found in the Netherlands.
The reviewer is right that the recent introduction of West Nile in the Netherlands should also be stated in the Introduction section. We have added the introduction of West Nile in lines 48-50.
Material and Methods (***): There are a lot of parameter values in this paper. A table detailing what each parameter is called, what it measures, where it comes from (user input or otherwise) would be very helpful as I was constantly scrolling up and down to remember what each thing was and how it was calculated. I did realise (too late) that there is a table of questions etc. in Appendix 1 which goes some way towards doing this but what I would really like to see is a Table in the main text with all there terms involved in the Equations included.
We compiled a table with an overview of parameters in MINTRISK as suggested by the reviewer and added this table (Table 1) to the main body of the manuscript.
Lines 86-88 (**): When I first read through this I was somewhat confused because it seems that transmission is simply the first half of establishment e.g. transmission is the ability of the pathogen to spread to vector to host and establishment is the ability of the pathogen to spread from vector to host and back again. This made me worry that the same thing may be modelled twice. I see now that it seems to be more of an either/or approach where the introduction risk score comes from entry*establishment if Ropt>1 but it can come from just the transmission term (Ropt) if Ropt<1. I wonder if this can be clarified earlier on to avoid my initial concerns/confusion?
MINTRISK is based on the FEVER framework. In that framework, we advised risk assessors to first estimate the steps for entry and transmission and to only proceed with the risk assessment if both of them were non-negligible. That’s why transmission is estimated in an early stage in MINTRISK as well. We have added a sentence explaining this rationale to the manuscript in lines 204-205. Furthermore, the reviewer’s comment made us realise that the position of the transmission and establishment steps were not correctly positioned in Fig. 1 and therefore we slightly amended Fig 1.
Line 162 (**): “frequency of epidemics per year”?
Yes indeed. We added “per year” to this line.
Eqn 2 (***): This is stated to be a probability but it is not possible that it could be >1? For example, Fepi=0.5, Area=1, HRPrr=3, Prev=0.7 gives Pepi_pt=1.05. Whilst this may be an unlikely set of values I don't see why it would be implausible?
The reviewer is right that, in theory, the value of Pepi_pt could be > 1. Whereas the values of Area and Prevepi_pt are always < 1, the values of Fepi and HRPRR are not. However, the latter two values are dependent on each other. If the frequency of epidemics would be > 1, then the length of the high risk period is by definition < 1 and vice versa. (The example above is thus not a realistic set of parameter values, suggesting one epidemic every 2 years, but detection of each epidemic only after a period of 3 years) Hence, a sensible set of input parameters will not result in a probability > 1 for Pepi_pt. Furthermore, values for Prevepi_pt are in general very low, even further reducing the possibilities for Pepi_pt being > 1. To ensure that the value of Pepi_pt does not exceed 1, we changed the equation in both the manuscript and the model code into:
P_(epi_pt)=Min(F_epi×Area×〖HRP〗_RR×〖Prev〗_(epi_pt),1)
Lines 188-189 (*): The specific question in MINTRISK is "What is the estimated value of the basic reproduction ratio?", which is less descriptive than the definition here. I think it would be preferable to use this description in the MINTRISK question, as R will be affected by vector-host ratio so it may be important for users to know the assumed context?
This has been accounted for in MINTRISK by providing additional information under the i-button. We added an additional comment in MINTRISK that R0 should be estimated for a fully susceptible host population. Users of MINTRISK are encouraged to read this additional information before answering each question.
Lines 189-191 (**): A 10% reduction in R0 when the vector is present in less than 5% of the area doesn't seem like very much - and likewise no reduction for anything more than 5% coverage doesn't seem like much. What's the justification for this choice? How sensitive are results to it?
The reviewer is right in that this will not result in a high reduction of R0 and hence will not severely affect MINTRISK’s estimate for transmission. This reduction value was included to account for those conditions where a vector is definitely not abundantly present. However, relationships between vector abundancy and transmission are not easy to incorporate in a generic model, since transmission might still be very efficient in those areas where the vector is present indeed. If the vector is present in a few areas, the transmission will go smoothly in those few areas, while there will be very slow spatial transmission in larger areas. If R0 is sufficiently high, the spill-over from one infected sub-area will lead to spread to other subareas with vectors, but with substantial delay. If R0 is low, i.e. close to 1, there will be a lot of opportunity for fade out, even if there would be multiple epidemic starts. Thus, a limited reduction of R0 appears to be the best way to easily incorporate this aspect. This question and parameter were built into MINTRISK to raise awareness with the risk assessor that these elements have to be considered when estimating transmission of vector-borne diseases. Although Dvector will contribute to a proper estimate for the transmission rate, it cannot account for the spatial and ecological differences within an area at risk that might either favour or hamper transmission.
We added an explanation on the parameterization of Dvector in lines 217-220.
Line 203 (*): Intuitively when I think of “introduction” I find myself thinking of what has been described in the paper as “entry”. I think it would be easier to follow if this section talked about “introduction and establishment” (although I appreciate that doesn’t fit nicely into the equations).
Thanks for noting this. We agree that entry and introduction are very close (maybe even synonyms) and that using introduction for the successful entry of a vector-borne disease (i.e. entry and establishment) might be somewhat confusing. We have tried to avoid confusion by giving clear definitions (lines 91-100) and providing Fig 1. However, upon rereading we realised that it might be helpful for the reader to include definitions for the summarizing output parameters as well at the start of the model description. These were therefore added to the manuscript in lines 101-106, directly after the definitions of the individual steps.
Lines 227-230 (**): So am I correct that each individual result is based on only a single pathway (though that pathway could be one of up to three for any given run)? Could this not lead to quite severe underestimation of risk i.e. if there are three pathways all with similar risk will that not lead to a higher overall risk (because it would be cumulative) than the case where there is one pathway with this risk and the other two have negligible risk?
The reviewer is correct that results of each iteration are based on a single pathway only, and that this pathway can vary among the (max 3) pathways selected. Since model input and output is given on a logarithmic scale, the pathway with the highest rate of introduction will in general drive the overall risk estimate, even if results of the individual pathways were added. The reviewer is right that the risk would be slightly higher if all three pathways had comparable risk (which is not a very common scenario), but even then, it would be at most 0.5 times higher than the calculated risk based on a single pathway.
When revising the paper, we realised that the logarithmic scale of answer categories and output was not communicated very clearly in the manuscript (although given in S1 and S2 Appendices). We therefore added a comment on this in lines 116-118.
Lines 254-255 (**): I don't follow this logic. Why would long-distance spread of hosts or vectors mean more infection generations in a season (or vice versa). I can see how it would increase the population at risk but not the generation time. To be clear, when I read infection generation I'm essentially thinking of the length of a gonotrophic cycle - is that the definition here? I think there needs to be a bit more justification/clarification here.
These parameters (Overlap, Local, Movvector and Movhost) affect the rate at with which the infection will spread in the area at risk. The rate of transmission is both determined by the R0 value and the number of infection generations in a vector season. We have chosen to model the effect of these parameters on disease spread by reducing the number of infection generations in case these parameters are limiting the efficient transmission of the infection. Less infection generations (and thus a longer time span between infection generations) will reduce the rate of transmission in the vector season and therefore also the number of infected host animals (epidemiological units). We have added a brief explanation on how these parameters would affect the number of infection generations in lines 282-289.
An infection generation can be compared to a generation in population biology, based on R0. Each next generation of infections is a new infection generation. The time needed from a “parent generation” to its “offspring generation” depends on the latent and infectious period in host animals and the extrinsic incubation period and infectious period (� lifespan) in vectors. The number of infection generations in one vector season is based on these parameters and the estimated length of the vector season. We added this definition to the information on this question in MINTRISK under the i-button to ensure that the risk assessor has a good understanding of infection generations.
Lines 255 (**): Why 50%? How sensitive are the results to this choice?
This 50% is an arbitrary choice. We deemed it unrealistic to include a larger reduction effect. All those aspects lead to slower transmission, which we incorporate into fewer infection generations per year, but they cannot stop transmission as such, since there are many routes of transmission possible in these vector borne diseases. In most assessments, these four parameters (Overlap, Local, Movvector and Movhost) will only result in a slight reduction of the number of infection generations.
Eqn 11 (***): This just seems to add the value for Reff in each generation but surely the number of infectives at the start of the generation will need to be included each time. For example, if Reff was 5, there were 3 generations and HRP>Tseason then this formula would give 15 infections (I think); however surely it should be 5 new infections in generation 1, then each of those 5 infections produces 5 more in generation 2 (giving 5*5=25 new infections) and then those 25 each generate a 5 new infections (giving 25*5=125 new infections). So the total number of infections would be 5+25+125=155? Unless the idea is that it is a separate introduction for each generation season and so there are 5 secondary infections each generation season with no onward spread from there but then I think the text needs to be much clearer about is meant by spread because I would interpret that is repeated introductions rather than spread. What am I missing here? Note my same confusion reappears in Eqns 12 and 14.
Equations 11, 12 and 14 are pretty complex equations indeed. We will try to explain using
In explaining
Lines 295-297 (**): What is the justification for focussing only on the most likely route? If there were multiple potential routes it should be possible to determine the expected number of cases of overwintering by at least one of those routes. I think what's done is fine when it's expected that one route will dominate but if there are multiple likely routes then will it not underestimate risk?
Analogue to selecting only the pathway with the highest introduction score (see the reviewer’s comment for lines 227-230), we here also select only the overwintering route with the highest probability, because of the logarithmic scale at which input and output of MINTRISK is given.
Eqn 14 (**): I think I read somewhere that you only consider the first 3 years, which would explain the 3 at the top of the last summation, however I can’t find where I read that now. It would be good to mention that next to this equation. If I didn’t read that somewhere, why only sum to 3?
In the text below
Line 336 (**): Any justification for the choice of 100?
To calculate the economic impact of a zoonotic vector-borne disease, the economic costs due to human disease had to be offset to the number of infected animal hosts. Since humans are mostly spill over hosts, resulting in relatively few cases, we deemed it more easy to estimate the costs per 100 infected animals. The number of 100 was a bit of an arbitrary choice. It worked pretty well for RVF, since the number of expected human cases was estimated to be 1 for every 100 infected animals. For West Nile fever, the ratio between cases in humans and infections in birds is, however, lower with 1 expected human case for every 10^4 - 10^5 infected birds.
Line 346-351 (**): Why the maximum? I would have thought a cumulative measure of socio-ethical impact would be preferable in cases where one impact doesn’t dominate?
Analogue to selecting only the pathway with the highest introduction score (see the reviewer’s comment for lines 227-230) and to selecting only the overwintering route with the highest probability (see the reviewer’s comment for lines 295-297), we here also selected only the socio-ethical (and environmental) impact with the highest risk score, because of the logarithmic scale at which input and output of MINTRISK is given.
Lines 434-439 (**): Does this not suggest an issue with the model calibration if a high number of infections is able to drive a high economic impact even when the user has specified economic impact to be low? Has there been much evidence of economic impact in worse affected areas like Italy, for example?
Calculation of the economic impact in MINTRISK is partly based on the multiplication of the epidemic size with the costs per epidemiological unit (
Appendixes (**): Some explanation of the parameterisation of the log transformation would probably be helpful i.e. it is of the form a^((IV+b)*c) but it would be good to explain briefly how a, b and c are determined. Likewise for Appendix 2.
An explanation of the general equations used for the log-transformation and inverse log-transformation were added to footnote c in S1 Appendix and footnote a in S2 Appendix, respectively.
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Assessing the introduction risk of vector-borne animal diseases for the Netherlands using MINTRISK: A Model for INTegrated RISK assessment
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One small but important issue remains: Line 34 -36 Are the authors sure that this statements about likely route of virus by mosquitoes in an aircraft concerns West Nile virus and not Rift valley fever virus?
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Assessing the introduction risk of vector-borne animal diseases for the Netherlands using MINTRISK: A Model for INTegrated RISK assessment
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