Projecting the Potential Distribution Areas of Lutzomyia verrucarum and Lutzomyia peruensis using MaxEnt under Climate Change and Predicting the Risk of Carrion’s Disease

doi:10.21203/rs.3.rs-3733663/v1

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Projecting the Potential Distribution Areas of Lutzomyia verrucarum and Lutzomyia peruensis using MaxEnt under Climate Change and Predicting the Risk of Carrion’s Disease

https://doi.org/10.21203/rs.3.rs-3733663/v1

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Carrion’s disease is a vector-borne disease caused by Bartonella bacilliformis. The phlebotomine sand flies Lutzomyia peruensis and Lutzomyia verrucarum are the determined illness vectors. Some climate parameters, such as precipitation and temperature have affected the development and reproduction of sand flies. In addition, other climate related factors that influence the activity of sand flies have also been included in the model. This study used the maximum entropy (MaxEnt) model to evaluate the contribution of climate parameters, and the current and future potential geographical distributions of these two species in the world were estimated. ArcGIS 10.5 software was used to visualize the results, and R 4.2.2 software was used to select the optimal model parameters. We found that temperature seasonality and altitude contributed the most to the distribution of L. verrucarum, while isothermality and altitude contributed the most to L. peruensis. Under current climate conditions, the highly suitable areas of the two species are mainly distributed in the Andes region of South America and middle eastern Africa. Climate change of different intensities will mainly have a negative effect on the worldwide suitable areas of sand flies in the future. In addition necessary monitoring and preventive measures should be taken in high-risk areas with vectors. In addition to vectors, the population also plays an important role in the occurrence of Carrion’s disease, indicating that in areas with high population density, we should pay more attention to the prevention and control of Carrion’s disease.

Climate Change

MaxEnt

Carrion’s Disease

Sandfly

Carrion's disease is a biphasic vector-borne disease caused by B. bacilliformis. It includes two different phases. The acute phase, known as Oroya fever, is characterized by acute hemolytic anemia. In this phase the case-fatality rate can be as high as 88%(Gomes et al., 2016), which is related to secondary bacterial infections and high parasitemia. In addition, the chronic phase, involves blood-filled nodular hemangiomas of the skin, termed “Peruvian warts”(Minnick et al., 2014). B. bacilliformis proliferates in human blood by invading erythrocytes and is transmitted between humans through the bite of female sandflies. These persistently infected humans provide a host for sand flies, thus sustaining B. bacilliformis in nature. The disease is prevalent in the Andean cordillera in Colombia, Ecuador and Peru(Gomes & Ruiz, 2018) because of the restriction of vectors, but sporadic cases have also been reported in other areas. Research shows that L.verrucarum is the main vector in highlands, and L. peruensis is mainly distributed in the valleys between the central and northern Andes of Peru (Ruiz et al., 2018). Additionally, L.verrucarum has a crepuscular, endophilic feeding habit, and L. peruensis has anthropophilic habits and is opportunistic( Abraham G. Cáceres et al., 2001).

Climate change is the defining issue of our time and we are at a defining moment where the scope of its impacts is global and unprecedented in scale. The Intergovernmental Panel on Climate Change (IPCC)’s Sixth Assessment Report indicated that climate change is already affecting many extreme weather and climates in every region of the globe. Approximately 3.3 to 3.6 billion people live in contexts that are highly vulnerable to climate change. Although climate change is a complex phenomenon, there is no doubt that climate factors can affect the survival rate, abundance and distribution of vector organisms, and further affect the space-time distribution and prevalence of vector-borne diseases. Climatic parameters play a vital role in the development of sandflies, for example, rainfall intensity may affect the spawning and larval growth of female sandflies through direct mechanical action or microenvironmental changes(Avila-Jimenez et al., 2021), and extreme heat and environmental humidity can also affect the activity of sandflies(Negev et al., 2015). Influenced by climate change and other factors, many species of sandflies are expanding to new areas. Some scholars have predicted that the transmission vector of leishmaniasis L. flaviscutellata will expand to higher latitudes and altitudes under future climate conditions(Carvalho et al., 2015). L. peruensis was once considered to be distributed in the range of 1600 meters to 3250 meters above sea level, but it has also been reported in the area of 1000 meters and 3740 meters above sea level (Moo-Llanes et al., 2017).

As a vector-borne disease, the growth, reproduction, and activity of the vectors, L.verrucarum and L. peruensis, are the main influencing factors for the occurrence and transmission of the disease. There are also some factors that may be related to the prevalence of carrion’s disease. Combined with the fitness probability of the vector sandfly, this study also collected factors such as vegetation, land use, and population, and the risk probability of carrion’s disease could be calculated.

In recent years, species distribution models (SDMs) have been increasingly widely used in the control and management of invasive alien species(Peterson, 2003). They associate species distribution data with environmental variables to reveal the potential distribution of species, and thus find suitable survival areas of the species(Thuiller et al., 2005). The maximum entropy model (MaxEnt model) is currently widely used and considered the best species distribution model(Duan et al., 2014).

Some studies have explored the suitable area distribution of L. peruensis in Peru using SDMs. In addition, there are some studies on L.verrucarum and L. peruensis ecology, but most of these studies are limited to Peru and its surrounding areas in South America. At present, no one has studied the suitable area distributions of L.verrucarum and L. peruensis in the world and China under current and future climate conditions. Based on climate data and global distribution data of L.verrucarum and L. peruensis, this study uses the MaxEnt model to discuss the climatic factors related to the suitable area distribution of these two vectors, estimate their current and future distributions worldwide, and calculate the risk of Carrion’s disease, providing a reference for their monitoring and effective prevention and control.

2.1 Software and Map Data

In our study, Maxent 3.4.4 software was used to estimate the suitable habitat for L.verrucarum and L. peruensis, ArcGIS 10.5 software was used to visualize the results, and R 4.2.2 software and DIVA-GIS 7.5.0 were used to select the optimal model parameters. The world map (1:10 million) was downloaded from the Natural Earth (http://www.naturelearthdata.com), and the map of China (1:4 million) was downloaded from the website of the National Geographics Center of China(http://www.ngcc.cn/ngcc/).

2.2 Environmental Variable Screening

We have taken some measures to improve the accuracy of the model, including screening environmental variables and using the ENMeval package to select the best parameters for the model. The contributions of these 56 environmental variables were obtained after the first run of the model. Because certain environmental variables were strongly corrected, we used R software to conduct Pearson correlation analysis on those environmental variables with a contribution rate of first 20 to the model and ≥ 0.1, then selectively eliminating highly correlated variables (| r | > 0.8).

2.3 Model Procedure

Because the distributed point data are not abundant, to improve the accuracy of the model, we modify the feature classes (FC) and regularization multipliers (RM) using the ENMeval package in R software. There are five types of FC: linear (L), quadratic (Q), product (P), threshold (T), and hinge (H). The regularization coefficients are set at eight levels: 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0. The R software package ENMeval is used to obtain the optimal model through the Akaike information criterion (AIC), which is one of the criteria for evaluating the goodness fit of a model. When the AIC value is the smallest (0 in this article), it is the optimal model. The optimal parameters for these two sandflies in this study are as follows: FC = LQH, RM = 2.5 for L. verrucarum, and FC = LQ for L. peruensis. A total of 25.0% of the sample points were randomly selected as the test set, the remaining sample points were used as the training set, and the model was repeated for 20 times. The “bootstrap” method was used to resample (Table 1), and the jackknife method was used to measure the contribution of environmental variables, draw the receiver operating characteristic curve (ROC), and use the area under the curve (AUC) to measure the accuracy of the model. The AUC value range is 0–1, and the larger the value is, the higher the accuracy of the model.

Table 1

Parameter settings for the prediction model of these two species' suitable habitats
Options	parameters
Options	L.verrucarum	L. peruensis
Feature classes	linear, quadratic, hinge	linear
Random test percentage	25	25
Regularization multiplier	2.5	0.5
Replicates	20	20
Replicated run type	bootstrap	bootstrap
Maximum iterations	5000	5000
Apply threshold rule	10 percentile training presence	10 percentile training presence

2.4 Classification of Suitable Areas

Using the reclassification tool of ArcMap, the model estimation results are divided into four levels based on their probability of existence using the natural breaks (jenks) method: non risk areas, low risk areas, medium risk areas, and high risk areas.

3.1 Biological Distribution Data

Distribution data on L.verrucarum and L. peruensis were collected by the Global Biodiversity Information Facility (GBIF) (https://www.gbif.org, accessed on 19 June 2022) and the literature database(pubmed). When exact geographic coordinates are missing, we use Google Earth to determine its longitude and latitude based on the provided textual description of the geographic location. Store in Excel, delete some duplicate points and unreasonable coordinate points, and finally convert the data into CSV format.

3.1 Carrion’s Disease Case Distribution Data

We collected the distribution data of Carrion’s disease cases from the literature database and official website of the Peruvian Center for Disease Control and Prevention(https://www.dge.gob.pe/).

3.2 Environmental Data

In total, 19 bioclimatic variables(bio1-19), 36 climate variables of monthly minimum(tmin01-12) and maximum temperature(tmax01-12), monthly total precipitation(prec01-12) and elevation variables from 1970–2000 were downloaded from the WorldClim Global Climate Database (version 1.4) (http://www.worldclim.org/, accessed on 7 July 2021). Future climate variables were processed by the BCC-CSM2-MR model under four shared socioeconomic pathways (SSPs) 126, 245, 370, and 585 for the years 2021–2040, 2041–2060, 2061–2080, and 2081–2100. All these historic and future environmental data were at a spatial resolution of 2.5 arc-minutes (approximately 21 km²). In addition, net primary production (NPP) refers to the net carbon sequestration of green plants through photosynthesis, which is the remaining amount of organic matter produced by photosynthesis per unit time and area, after deducting autotrophic respiration. It can represent the surface vegetation situation to some extent, and it was at a spatial resolution of 30 arc-seconds (approximately 1 km²) and downloaded from the website of CHELSA (Climatologies at high resolution for the earth’s land surface areas)( https://chelsa-climate.org/).

3.3 Population Data

Using an innovative approach that combines geospatial science, remote sensing technology, and machine learning algorithms, LandScan Global is the finest resolution global population distribution data available representing an ambient (24 hour average) population. This study obtained the global population in 2022 from LandScan Global with a spatial resolution of 30 arc-seconds(https://landscan.ornl.gov/).

4.1 Species Distribution Data and Screened Environmental Variables

Twenty-eight L. verrucarum and 12 L. peruensis distribution data points were collected(Fig. 1), the climate data of these points were obtained using the ArcMap sampling tool, and correlation analysis was conducted (Fig. 2). Finally, 4 environmental variables for L.verrucarum(Table 2) and 5 environmental variables for L. peruensis (Table 3)were screened for the maxent. The accuracy of the model is very high. The model AUC value of L.verrucarum was 0.980, while that of L. peruensis was 0.986.

Table 2

The high contributing environmental variables of the potentially suitable area of *L.verrucarum*
Environmental variables	Connotation	Percent contribution
bio4	Temperature Seasonality (standard deviation ×100)	68.3
elev	Elevation	16.6
prec01	Precipitation of January	8.6
prec05	Precipitation of May	8.6
Bio7	Temperature Annual Range (bio5-bio6)	2.8

Bio5: Max temperature of warmest month; bio6: Min temperature of coldest month.

Table 3

The high contributing environmental variables of the potentially suitable area of *L. peruensis*
Environmental variables	Connotation	Percent contribution
bio3	Isothermality (bio2/bio7) (×100)	62.4
elev	Elevation	19.4
prec09	Precipitation of September	15
bio15	Precipitation Seasonality (Coefficient of Variation)	2.3
prec10	Precipitation of October	0.9

Bio2: Mean diurnal range (mean of monthly (max temp - min temp).

Many studies have shown that the growth, reproduction, and behavior of infectious disease vectors, including the vector of carrion’s disease, sandflies, are sensitive to climate factors such as temperature, precipitation, and humidity, and with climate change, many vectors have experienced varying degrees of expansion (C et al., 2010; Caminade et al., 2019; H et al., 2008; Semenza & Suk, 2018). In our study, for L.verrucarum, bio4(temperature seasonality) provided the highest contribution percentage of 68.3%, which is the standard deviation of monthly average temperature, and the larger its value is, the lower the probability of L.verrucarum suitable habitat in the range of 0-500. When it exceeds 500, the probability is close to 0 (Fig. 3); that is, the larger the monthly average temperature difference is, the less suitable it is for L. verrucarum to survive within limits. The study by Luis Fernando Chaves et al. shows that when the temperature suddenly cools or heats up, the average number of sandflies significantly decreases(Chaves et al., 2014). It is probably a sudden high or low temperature that leads to a decrease in sand fly adaptability to the environment. However for L. peruensis, bio3(isothermality) provided the highest contribution percentage of 62.4%, Within the isothermality range of 50–90, as the isothermality increased, the probability of L. peruensis suitability increased (Fig. 4). In other words, the closer the daily and annual temperature ranges are, the more suitable they are for L. peruensis to survive. The main factors affecting the mean diurnal air temperature range are latitude and season, and the lower the latitude is, the greater the mean diurnal air temperature range(Jaagus et al., 2014). The factors affecting the annual range of air temperature are mainly latitude, sea and land distribution; conversely, the lower the latitude is, the smaller the annual range of air temperature. L. peruensis is limited to Peru and certain western Andean valleys(Zorrilla et al., 2017). These regions have low latitudes, which may be the main reason for the high suitability areas in a small number of central African regions in the estimates.

In addition, altitude is another important factor affecting the distribution of suitable habitats for these two species, with altitude having a more significant impact on L. peruensis. Some studies have shown that L.verrucarum can survive in areas from 1100 to 3200 m sea level(Caceres, 1993), while L. peruensis predominates above 2250 m and can extend upward to areas at 3250 m above sea level(Hashiguchi et al., 2018). The high altitude of the Tibet Autonomous Region in China may be the main reason why there was a small number of suitable areas here.

The other environmental variables that affect the distribution of suitable areas for L. peruensis are all related to precipitation, namely prec09 (precipitation in September), prec10 (precipitation in October), and bio15(precipitation seasonality). Studies have shown that the distribution of L. peruensis follows a single peak annual time distribution pattern, with its peak occurring in November before the onset of the rainy season in December(Minnick et al., 2014). However, the results of this study indicate that the higher the precipitation in September is, the lower the probability of L. peruensis adapting, indicating that if the rainy season is abnormally early, it may lead to a decrease in the density of L. peruensis. Since the 1980s, there has been a decrease in precipitation from September to October on a ten-year scale(Espinoza Villar et al., 2009), which may also be one of the reasons why the survival of L. peruensis is more sensitive to September precipitation. Peru has diverse climate and terrain. It is divided into eastern and western parts by the Andes Mountains. The western part has a low altitude and is close to the Pacific Ocean. Affected by the dehumidification effect of the Peru cold current, it has formed a long and narrow tropical desert area. The eastern part has a high altitude and is dominated by tropical rainforest and grassland climates. However, due to the strong influence of the Atlantic Ocean, the seasonal intensity of precipitation in Peru is not particularly strong(Gaudry et al., 2017), so the survival probability of L. peruensis is not significantly affected by precipitation seasonality.

Figure 4 Response curves of environmental variables to the distribution probability of L. peruensis (a) Isothermality (bio2/bio7) (×100) (b) Elevation (c) Precipitation of September (d) Precipitation Seasonality (Coefficient of Variation) (e) Precipitation of October, and the curves shown are averages over 20 replicate runs, blue margins show the ± SD calculated over 20 replicates

4.2 World Distribution of L.verrucarum and L. peruensis Risk Areas

4.2.1 Distribution of Current Risk Areas for L.verrucarum and L. peruensis

The model estimated that L.verrucarum currently has a total area of 16.3 million km² in the world, accounting for 10.9% of the total continental area, while L. peruensis currently has a total area of 16.4 million km² in the world, accounting for 11.0% of the total continental area. The estimated results show that both species have the highest risk of adaptation in the Andean regions of northern, central, and southern Peru, consistent with the research of Victor Zorrilla et al(V et al., 2017). For L.verrucarum, the high risk areas are mainly located in the western region of the Andes Mountains in South America, central Africa and the equatorial south central region of Indonesia. The medium risk areas are mainly distributed in parts of central Africa south of the equator. Low risk areas are mainly found in northern South America, covering almost the entire territory of Brazil and South Africa, and are scattered in some Southeast Asian countries (Fig. 5). Compared to L.verrucarum, in western South America, the strip-like high habitat of L. peruensis is generally more southward oriented, with more medium risk suitable areas in Brazil, but the areas of low habitat are significantly smaller than those of L.verrucarum. In Africa, the clustered medium and high risk suitable areas are significantly less abundant, but the low suitability areas in southern Africa are visibly more abundant than those in L.verrucarum. Additionally, it is worth mentioning that there are also a small number of low suitability areas and minute quantities of medium suitability areas in Chinafor L. peruensis (Fig. 6).

4.2.2 Distribution of Future Risk Areas for L. verrucarum and L. peruensis

The future projections of L. verrucarum indicated larger climatic adaptation areas than L. peruensis. Under the lowest radiative forcing of the SSP1-2.6 scenario, the change in L. verrucarum's global adaptive areas is mainly concentrated in the medium risk suitable areas in South America, which gradually decreases. Under the highest radiative forcing of the SSP5-8.5 scenario, the medium and low risk suitable areas in South America and the medium and high risk suitable areas in Africa are gradually reduced. Under the moderate radiative forcing scenarios of SSP2-4.5 and SSP3-7.0, the single trend of change in suitable areas in each region is not significant, but globally, under the SSP2-4.5 climate scenario, the area of suitable habitat slightly decreases, while under the SSP3-7.0 climate scenario, the area of medium and high risk areas first increases and then decreases, while the area of low-risk areas shows a decreasing trend (Fig. 7). Overall, under various climate scenarios in the 21st century, the suitable areas of L. verrucarum worldwide have only increased in a few cases, with most still showing a decreasing trend (Fig. 8).

Figure 8 The future suitable area changes in L. verrucarum compared with the current suitable areas under the four shared socioeconomic pathways: (a) SSP1-2.6, (b) SSP2-4.5, (c) SSP3-7.0, and (d) SSP5-8.5

Compared with the current situation, in the 21st century, the areas of the global suitable habitat of L. peruensis have decreased (Fig. 9), and the most obvious reduction is the low suitable habitat in southern Africa. For the estimation of the suitable habitat of L. peruensis, under the lowest radiative forcing of the SSP1-2.6 scenario, the most crucial thing is the emergence of high suitable habitat in the coastal area of northeastern Brazil, which shows an increasing trend over time, while under the scenario of SSP5-8.5 of the highest radiative forcing, the area of the medium and low suitable habitat in Brazil and the low suitable habitat in Africa increases. Under the moderate radiative forcing scenarios SSP2-4.5 and SSP3-7.0, the change in the mid-2000s is not obvious, but the suitable habitat under the SSP2-4.5 scenarios 2081–2100 and SSP3-7.0 scenarios 2021–2040 is significantly wider than that in the other periods (Fig. 10).

This study indicates that for most of the future of the 21st century, the total suitable habitat of the two species of sandflies will generally show a decreasing trend. However, there was a significant increase in the moderately suitable habitat of L.verrucarum in South America and in the highly suitable habitat of L. peruensis in Brazil, which may be related to climate change factors such as rising global temperatures and increasing extreme weather. Peru will be strongly affected by the El Niño phenomenon; when El Niño arrives, the usually arid areas in northern Peru suddenly experience heavy rainfall. Some studies have shown that the Tumbes, the northernmost part of the Peruvian coast, receives over 200 times the annual rainfall during the El Niño period compared to non-El Niño periods(Bayer et al., 2014). Under the possible impact of climate change caused by El Niño, the number and area of cases of carrion’s disease have significantly increased(E et al., 2004). However, with the implementation of vector control measures, the increase in the number of cases has been alleviated(Maguiña Vargas & Pachas, 2014). These phenomena all indicate that climate change may have a positive impact on the density of the vector of Carrion’s disease, leading to an increase in population risk.

Figure 9 The future suitable area changes in L. peruensis compared with the current suitable area under the four shared socioeconomic pathways: (a) SSP1-2.6, (b) SSP2-4.5, (c) SSP3-7.0, and (d) SSP5-8.5

4.3 Comprehensive Risk of Carrion’s Disease in High-risk African Countries

Both of these sandflies have highly suitable areas in Africa, mainly in The Federal Democratic Republic of Ethiopia, The Republic of Kenya, The United Republic of Tanzania, The Republic of Uganda, Democratic Republic of the Congo, The Republic of Burundi and Republic of Rwanda. The fitness probabilities of these two types of sandflies in these countries are shown in Table 4. Based on the vegetation, landcover, and population data of these countries, the risk level of Carrion’s disease calculated by the model is shown in Fig. 11, and the risk value is shown in Table 3. At this time, the contribution of the probability of the presence of L.verrucarum contributes the most to the risk of disease occurrence, followed by population density(Table 4). The epublic of Rwanda has the highest risk of disease occurrence, followed by The Republic of Burundi.

Table 4

Survival probability of *L.verrucarum* and *L. peruensis* and incidence risk of Carrion’s disease in these 7 countries
Country	L.verrucarum	L. peruensis	The risk of disease
Republic of Rwanda	0.382	0.337	0.880
The Republic of Burundi	0.354	0.256	0.509
The Federal Democratic Republic of Ethiopia	0.142	0.104	0.284
The Republic of Kenya	0.115	0.383	0.138
Democratic Republic of the Congo	0.254	0.013	0.025
The United Republic of Tanzania	0.071	0.127	0.019
The Republic of Uganda	0.040	0.013	0.004

Table 5

The contribution of variables to the model
Variables	Contribution
L.verrucarum	56.7
population	16.7
L. peruensis	15.8
landcover	7.6
npp	3.2

This study is the first to use climate factors to estimate the suitable habitat of the vectors of Carrion’s disease pathogens, L. verrucarum and L. peruensis. The results show that the adaptability of L.verrucarum is most affected by temperature seasonality, while L. peruensis is most affected by isothermality. At the same time, their survival is closely related to altitude. In addition, the adaptability of L. peruensis is more influenced by precipitation-related factors. The high suitability areas of both species of sandflies are distributed in the Peruvian Andes and a small part of central Africa. Due to the impact of El Niño, the climate in Peru may experience abnormalities; when abnormalities occur, people need to pay more attention to the control of vector sand flies and decrease their bites. Although the elevation in central Africa is relatively low, due to its proximity to the equator, the annual temperature change is relatively small, so there is also a risk of vector sand flies growing. The warty and swollen sand fly is the main vector that affects the distribution of diseases, while the Peruvian sand fly is the secondary vector that affects the distribution of diseases. In addition to the two vectors, population density is also an important factor affecting the distribution of diseases. Although the vectors have not yet been found, it is also necessary to strengthen monitoring and prevent the entry and colonization of L.verrucarum and L. peruensis, especially in the Republic of Rwanda and The Republic of Burundi .

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Consent for Publication

Not applicable

Availability of Data and Materials

All data generated or analysed during this study are included in this published article.

Competing Interests

The authors declare that they have no competing interests.

Funding

This study was funded by the National Key Research and Development Program of China(2020YFC1200101).

Authors’ Contributions

All authors contributed to the study conception and design. Material preparation, data collection, analysis and visualization were performed by X.W., N.C., K.L., Z.W. and C.G. The first draft of the manuscript was written by X.W. and all authors commented on previous versions of the manuscript. Supervision of this research was provided by Q.L. All the authors have read and approved the final manuscript.

Acknowledgements

Not applicable

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No competing interests reported.

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Projecting the Potential Distribution Areas of Lutzomyia verrucarum and Lutzomyia peruensis using MaxEnt under Climate Change and Predicting the Risk of Carrion’s Disease

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Abstract

Figures

1 Introduction

2 Materials and Methods

2.1 Software and Map Data

2.2 Environmental Variable Screening

2.3 Model Procedure

2.4 Classification of Suitable Areas

3 Data

3.1 Biological Distribution Data

3.1 Carrion’s Disease Case Distribution Data

3.2 Environmental Data

3.3 Population Data

4 Results

4.1 Species Distribution Data and Screened Environmental Variables

4.2 World Distribution of L.verrucarum and L. peruensis Risk Areas

4.2.1 Distribution of Current Risk Areas for L.verrucarum and L. peruensis

4.2.2 Distribution of Future Risk Areas for L. verrucarum and L. peruensis

4.3 Comprehensive Risk of Carrion’s Disease in High-risk African Countries

5 Conclusions

Declarations

References

Additional Declarations

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