# Assessing the Risk of Occurrence of Bluetongue in Senegal

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## Abstract

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## 1. Introduction

_{0}) as a measure of BT epidemic potential and mapped its values. The transmission dynamics are described using a compartmental model in a multi-species context, and we used the Next-Generation Matrix [26] approach to determine R

_{0}. Finally, we combined previous results with mobility and serological data to estimate the risk of occurrence of the disease in the different parts of Senegal due to the introduction of animals from infected zones [27,28].

## 2. Materials and Methods

#### 2.1. Study Area

#### 2.2. Data Collection

#### 2.2.1. Entomological Data

#### 2.2.2. Serological Data

#### 2.2.3. Climatic, Environmental and Livestock Data

#### 2.2.4. Small Ruminants Demographic Data

#### 2.2.5. Mobility Data

#### 2.3. Estimated Vector Abundance, R_{0}, and the Probability of the Occurrence of an Outbreak

#### 2.3.1. Abundance Modeling: Random Forest

#### 2.3.2. Model Host-Vector for Bluetongue Transmission

_{h}), animals either die (D) with a certain probability p, or recover I and acquire lifelong immunity with probability 1-p. Similarly, for the vector population, after infection, susceptible vectors (S) become latent (E), and then infectious for the rest of their lives (I). On top of the transmission dynamics, we considered the demographic dynamics, with animals and vectors being born and dying naturally. Animals born from recovered mothers are protected by maternal antibodies (Imm

_{h}) for the first three months of their lives before becoming susceptible. The resulting compartmental model is a system of nine ordinary differential equations reported in Supplementary Information. A pictorial representation of the model is shown in Figure 3, where the pedex h corresponds to the host-related compartments, and the

**v**one to the vector ones.

_{h}) and for the vectors (λ

_{v}) is estimated as:

_{vh}, P

_{hv}are the probabilities of transmission from vector to host and to host to vector, respectively, ${a}_{s}$ is the biting rate, ${I}_{h},{I}_{v}$ the number of infectious hosts, and vectors ${N}_{h},{N}_{v}$ their populations.

#### 2.3.3. Sensitivity Analysis

#### 2.3.4. Occurrence Probability Map

## 3. Results

#### 3.1. Estimating the Vector Spatial Abundance

#### 3.2. Estimating the Spatial Distribution of R_{0} (Transmission Maps)

_{0}is high in Casamance (mean ${R}_{0}=2.74,1.74$ for the temperature-independent and dependent case) and in the southeastern corner of Senegal (Kédougou region). Indeed, in the latter, the basic reproduction number is higher than 10. Similarly, in Casamance, the value of ${R}_{0}$ is high (above 5 in almost all the area)—possibly linked to the high abundance of C. enderleini. On the other hand, in the northern part of the country (particularly in the Ferlo area), the value of ${R}_{0}$ is low (mean ${R}_{0}=0.66,0.48$ for the temperature-independent and dependent case), meaning there is little risk of an outbreak of BT. Moreover, the map built using temperature-independent values predicts higher values of ${R}_{0}$ in the Casamance area, the groundnut basin (Bassin arachidier) (mean ${R}_{0}=0.80,0.51$ for the temperature-independent and dependent case) and along the Senegal River Valley in the north (mean ${R}_{0}=1.08,0.74$ for the temperature-independent and dependent case), than the map built using temperature-dependent values. The emerging trends reflect the distribution of Culicoides, and any discrepancies between the two approaches could be linked to the different biting rates and latency period. Indeed, areas with high vector abundance (Figure 4) present the highest value of ${R}_{0}$ due to the high vector-host ratio ($\frac{{N}_{V}}{{N}_{h}}$), while areas with low vector abundance (e.g., the Ferlo area) have the lowest risk of Bluetongue transmission.

#### 3.3. Estimating the Risk of the Occurrence of BT Outbreaks

## 4. Discussion

_{0}were likely to experience outbreaks with a high probability of occurrence in the south. However, the analysis of the map shows that livestock mobility could play an important role in triggering epidemics. In areas with low ${R}_{0}$ values (between 1 and 2), an epidemic can still occur if enough infected animals are introduced. This is the case in the Ferlo area and at the border with Gambia where there is a high probability of occurrence, despite their low ${R}_{0}$ values. Therefore, despite the fact that BT is a non-contagious disease, livestock mobility can play an important role in its diffusion. To validate our results, we need more information on the epidemiological situation in the region: In fact, we are unable to distinguish if our seroprevalence results are related to epidemic dynamics or low noise circulation or if the animals that were imported were already infected or not. Although our livestock data include information on international movements, in this article, we focused on national movements, since no information was available on the BT epidemiological situation in neighboring countries. Being able to include such, the information would make it possible to assess the risk of introducing the disease from other countries

## 5. Conclusions

## Supplementary Materials

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Seroprevalence, entomological, and demographic data. The circles show regional BT seroprevalence based on data collected during the national sero-survey in 2018. The squares show the distribution of the four Culicoides species of interest; the square’s size is proportional to the logarithm of the number of Culicoides collected during the national entomological survey in 2012. The background colors (red and blue) delimit the two pastoral zones with different birth and mortality rates. Dashed lines delimit the six agro-ecological zones in Senegal (Ferlo, Senegal River Valley, Eastern Senegal, Casamance, Groundnut Basin, Niayes), while the thin solids lines identify the administrative regions.

**Figure 2.**Mobility data. On top (

**a**) representation of livestock mobility network, each line corresponds to movements existing between the department, and the color is relative to the number of animals exchanged. The bottom part, departments are colored based on: (

**b**) The incoming volume (number of animals arriving in the department); (

**c**) outgoing volume (number of animals leaving the department), na: data not available.

**Figure 3.**Pictorial representation of the mathematical model. Host dynamics are at the top of the figure, vector dynamics at the bottom.

**Figure 4.**Maps of abundance of the four main Bluetongue virus (BTV) vector species in Senegal: (

**a**) C. imicola; (

**b**) C. oxystoma; (

**c**) C. enderleini, and (

**d**) C. miombo.

**Figure 5.**Transmission maps (${R}_{0}$) (

**a**) realized using temperature-independent parameters; (

**b**) using temperature-dependent parameters.

**Figure 6.**Tornado plot of sensitivity analysis in the 2 cases when parameters used in the model are temperature-independent (

**a**) or temperature-dependent (

**b**). The name of the epidemiological parameters that are varied on shown on the y-axis. The variation in the number of pixels at risk is shown on the x-axis. For each parameter, the bars represent the variation in the number of at risk pixels following a decrease or increase in a parameter value. The color of the bar indicates if the variation in the number of pixels at risk is due to an increase (red) or a decrease (blue) in the corresponding parameter value. The length of the bar is proportional to the variation in the number of pixels at risk (expressed as a percentage).

**Figure 7.**Probability of outbreaks due to the introduction of infected animals. (

**a**) using R

_{0}temperature-independent values, (

**b**) using R

_{0}temperature-dependent values. Livestock movements from infected areas could explain why areas with a low value of R

_{0}could have a high risk of outbreak occurrence. White areas correspond to areas where the outbreak probability has not been estimated because of missing information.

Parameter | Description | Temperature Independent | Temperature-Dependent | ||
---|---|---|---|---|---|

Values | References | Values | References | ||

${d}_{v}$ | Vector mortality rate | 0.16 [0.1–0.5] | [36,37] | $0.009{e}^{0.16T}$ | [36] |

b_{v} | Vector fertility rate | 6.1 eggs/year | [38] | ||

α_{v} | Latency rate for vector | 0.09 [0.06–0.1] | [33,36] | $0.0003T\left(T-10.4\right)$ | [35,36] |

P_{vh} | Transmission Vector-host | 0.9 [0.8–1.0] | [36] | ||

a_{s} | Biting rate | 0.17 [0.05–0.4] | [36] | $0.0002\times T\times \left(T-3.7\right)\times {\left(41.9-T\right)}^{\raisebox{1ex}{$1$}\!\left/ \!\raisebox{-1ex}{$27$}\right.}$ | [36] |

d_{h} | Host mortality rate | 0.0005 (Sylvo-pastoral area) 0.0008 (Agro-pastoral area) | [39] | ||

b_{h} | Host fertility rate | 0.002 (Sylvo-pastoral area) 0.0015 (Agro-pastoral area) | [39] | ||

α_{h} | Latency rate for host | 0.0625 | [33] | ||

r_{h} | Recovery rate for host | 0.125 | [27,36] | ||

d_{h} | Host mortality rate | 0.0005 (Sylvo-pastoral area) 0.0008 (Agro-pastoral area) | [39] | ||

b_{h} | Host fertility rate | 0.002 (Sylvo-pastoral area) 0.0015 (Agro-pastoral area) | [39] | ||

α_{h} | Latency rate for host | 0.0625 | [33] | ||

r_{h} | Recovery rate for host | 0.125 | [27,36] | ||

P_{hv} | Transmission Host Vector | 0.05 [0.001–0.15] | [36,37] | ||

p_{h} | Disease induced mortality | 0.01 [0.001–0.01] | [36] | ||

${\eta}_{h}$ | Rate of immunity loss | 1/90 | [39] |

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## Share and Cite

**MDPI and ACS Style**

Gahn, M.C.B.; Niakh, F.; Ciss, M.; Seck, I.; Lo, M.M.; Fall, A.G.; Biteye, B.; Fall, M.; Ndiaye, M.; Ba, A.; Seck, M.T.; Sall, B.; Lo, M.; Faye, C.; Squarzoni-Diaw, C.; Ka, A.; Amevoin, Y.; Apolloni, A. Assessing the Risk of Occurrence of Bluetongue in Senegal. *Microorganisms* **2020**, *8*, 1766.
https://doi.org/10.3390/microorganisms8111766

**AMA Style**

Gahn MCB, Niakh F, Ciss M, Seck I, Lo MM, Fall AG, Biteye B, Fall M, Ndiaye M, Ba A, Seck MT, Sall B, Lo M, Faye C, Squarzoni-Diaw C, Ka A, Amevoin Y, Apolloni A. Assessing the Risk of Occurrence of Bluetongue in Senegal. *Microorganisms*. 2020; 8(11):1766.
https://doi.org/10.3390/microorganisms8111766

**Chicago/Turabian Style**

Gahn, Marie Cicille Ba, Fallou Niakh, Mamadou Ciss, Ismaila Seck, Modou Moustapha Lo, Assane Gueye Fall, Biram Biteye, Moussa Fall, Mbengué Ndiaye, Aminata Ba, Momar Talla Seck, Baba Sall, Mbargou Lo, Coumba Faye, Cécile Squarzoni-Diaw, Alioune Ka, Yves Amevoin, and Andrea Apolloni. 2020. "Assessing the Risk of Occurrence of Bluetongue in Senegal" *Microorganisms* 8, no. 11: 1766.
https://doi.org/10.3390/microorganisms8111766