A Digital Twin Simulator Approach as a Support to Develop an

Integrated Observatory of the Epidemic Risk in a Rural Community

in Senegal

Jean Le Fur

, Moussa Sall

and Jean-Marie Dembele

Institut de Recherche pour le Développement (IRD), Centre de Biologie pour la Gestion des Populations (CBGP),

Campus Baillarguet, CS 30016, F-34988 Montferrier-sur-Lez, France

Dépt. Informatique, Univ. G. Berger/Saint-Louis, Senegal

Keywords: Digital Twin, Epidemic Risk, Agent-Based Model, Data Driven Approach, EcoHealth Approach, Synthetic

Ecology, Complex System.

Abstract: Following the contemporary epidemiologic approach known as EcoHealth, the study of an epidemic risk must

consider and integrate the whole set of actors, factors and environments bound to the transmission of

infectious diseases. In this study, we propose using a mechanistically rich digital twin simulator as a tool to

facilitate this integration with the addition of a functional and dynamic dimension. The selected case study is

the monitoring of the risk associated with ticks and rodents in a rural community in the Sahelian region of

Senegal. To construct the digital twin, we iteratively went back and forth between field data collection and

computer transcription of knowledge. Thanks to the high resolution afforded by the digital twin approach,

the simulator enables the study of city-scale activity patterns as well as interactions between ticks, rodents,

cats, and humans that occur within habitation rooms and shops. In addition to (i) being able to provide

dynamic integrated support for the collected multidisciplinary knowledge, the digital twin realism provides

(ii) an appropriate medium for communicating results to non-expert populations and (iii) a useful tool for

monitoring and adjusting the observatory's data collection protocols. The model's complexity presents

calibration challenges that are discussed.

1 INTRODUCTION

Approximately two-thirds of known infectious

diseases in humans are zoonoses, which are diseases

whose pathogens are transmitted between vertebrate

animals and humans (Jones et al., 2008). These

diseases typically entail a multitude of interacting

actors. In the typical instance of tick-borne diseases

such as Lyme disease (Adrion, 2015) or Borreliosis

(Elbir et al., 2015), the interacting agents are

pathogenic bacteria, tick vectors of these diseases,

intermediate rodent hosts that serve as reservoirs for

pathogens, predators (e.g., cats) that regulate

populations, and finally, humans susceptible to

infection. All of these agents interact within diverse

environments on varied length scales (e.g., nests or

burrows of rodents, rooms or market places).

The inseparable interconnection of the components

within such complex systems is evident and requires

integrated approaches to human and animal health

and their respective social and environmental

contexts (Zinsstag et al., 2011). The so-called

EcoHealth (Lisitza and Wolbring, 2018) or

OneHealth (Mencke, 2013) approaches suggest

jointly taking into account the knowledge produced

by various thematic disciplines to better account for

the processes at work in the transmission or lack

thereof of a disease. However, a number of authors

(e.g., Lefrançois et al., 2023, Rotureau et al., 2022)

point out that it is still challenging to implement such

multidisciplinary approaches due to the number of

factors to be considered, scientific disciplines to be

involved and the difficulty of integrating them.

In this process of articulating acquired knowledge,

modern modelling and simulation tools, and

particularly the object paradigm derived from

computer science, are important assets. Indeed, in

terms of the study of such complex systems,

numerical experiments allow for the articulation of

134

Le Fur, J., Sall, M. and Dembele, J.

A Digital Twin Simulator Approach as a Support to Develop an Integrated Observatory of the Epidemic Risk in a Rural Community in Senegal.

DOI: 10.5220/0012135900003546

In Proceedings of the 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications (SIMULTECH 2023), pages 134-142

ISBN: 978-989-758-668-2; ISSN: 2184-2841

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

various types of determinants and the comprehension

of the potential outcomes of experiments that are

difficult or impossible to conduct in situ (Auffray et

al., 2011).

To implement such an approach, the observed reality

must be represented in the most accurate manner.

This is the objective of the approach in terms of

digital twins (Tao et al., 2022). Usually devised to

account for complicated (industrial processes,

factories, robots) devices, the approach in terms of

digital twin can also be considered as a metaphor that

can be adapted to account for complex phenomena

(e.g., Nie and al., 2023) including those involving

actors in society (Grieves and Vickers, 2017 in White

et al., 2021). The major outcome expected with the

use of such an approach is to produce artefacts that

are close enough to known reality to allow the

processes to be studied in as much detail as possible,

particularly at a very high spatial resolution. Since the

occurrence or not of an infection of humans, rodents,

ticks depends on the unique configuration of each

dwelling, room, or activity, the digital twin paradigm

was felt as a promising approach to deal with an

EcoHealth approach of the epidemic risk.

In this work, we present the digital twin approach that

we have developed to represent and exploit the

knowledge acquired in the context of developing an

EcoHealth-type integrated observatory of the

epidemic risk in a rural African community.

2 MATERIAL AND METHOD

2.1 Model Purpose

We propose in this work to set up an integrative tool

allowing to articulate abiotic, trophic, physiological,

behavioural, social, demographic and environmental

factors involved in the spread of epidemics in a

typical rural Sahelian community in Senegal.

However, rather than storing the collected data within

a static database management system, the knowledge

gathered on the various aspects of the system studied

is placed into dynamic relation through a simulator

that accounts for behaviours and interactions. The

issue is concretely of developing a model as realistic

as possible by using a so-called "mechanistically

rich" approach (DeAngelis and Mooij, 2003) bringing

together the knowledge from diverse thematic

disciplines such as bio-ecology, parasitology,

geography urban, social and human sciences.

The approach is also founded on a data-driven

strategy for which the model's primary purpose is not

to replicate the actual dynamics, but rather to serve as

a repository for retrieving domain-specific

knowledge. It aims, for instance, to identify the

components for which there is a knowledge gap and

to serve as a forum for discussion between scientists

from different disciplines as well as between

scientists and stakeholders (population, decision-

makers) to ensure that the observatory and its

protocols are continuously improved. The subsequent

objective is to gain a simulation tool capable of

simulating a variety of epidemic risk evolution

scenarios (from pathogens to humans, from shops to

burrows).

2.2 Case Study and Modelling Platform

The pilot site that has been selected to develop the

EcoHealth observatory is the town of Dodel

(16°29'10.1"N 14°25'56.5"W), a typical rural

community of the Sahel in northern Senegal. In the

chosen case study, the emphasis was placed on the

risk associated with the transmission of Borreliosis or

relapsing tick-borne fever, as described in the

introduction.

The simulation model used as support for this

work has already been developed and presented in Le

Fur et al. (2017, 2021). The model is developed using

Java and the agent-based technology. It is developed

using the Repast-Simphony platform (North et al.,

2013). Based on a bio-inspired approach (Le Fur et

al., 2023), it enables the representation of synthetic

ecologies that include a variety of animal and human

biological agents with their own characteristics,

capacities for observation, deliberation, action and

interaction. The model is based on a parsimonious

data system using chronograms of various events. It

uses a double formalisation of space with a

continuous Euclidean space for processing moves

combined with a discrete grid for reifying the

environment's constituent objects. The time step of

the model is configurable in the sense that changes in

time step are passed on to all the formalized dynamic

processes.

2.3 Achievement

The development of a digital twin of the city of Dodel

has been predicated on the principle of continuous

improvement. It entails to incrementally build the

model by performing round trips between the

acquisition of multidisciplinary data in the field and

the digital transcription of the collected information.

The simulator model hence progressively gathers and

connects, in an integrated and dynamic manner, the

most possible complete set of available data and

A Digital Twin Simulator Approach as a Support to Develop an Integrated Observatory of the Epidemic Risk in a Rural Community in

Senegal

135

knowledge on the various factors influencing the risk

of epidemics.

In this section, for each of the acquisition stages,

we detail the fieldwork that was performed, then

describe the formalisation that was retained and

developed in order to construct, in parallel, the

multidisciplinary observatory and its supporting

simulator.

2.3.1 Cartography

A high-resolution map of the study area was the initial

step. A team consisting of a modeller and geographer

conducted a two-week survey of the entire city centre.

Concessions, buildings, rooms and remarkable points

have been mapped. A survey was conducted on each

property to determine the number of structures, the

type of fence, and, most importantly, the number,

type, and purpose of each room. In accordance with

the logic of the digital twin, the readings included the

interior of homes in order to have the most accurate

representation of the rooms and corridors where

humans, pathogens, reservoirs, or vectors may come

into contact.

The collected data were then georeferenced with

a geographic information system, rasterized with a 1

m resolution, and incorporated into the simulator as a

grid where each cell represents an object with

attributes (type of place, presence of food, etc.). A

dedicated algorithm then identified contiguous

clusters of cells in order to determine the functional

level of the various spaces (ex: all cells contained in

a room are gathered in a higher level object of room

type).

2.3.2 Population Census

The resident population census was conducted at the

same time. This effort resulted in the identification of

all downtown residents and their respective

occupations. Each resident was then reified in the

simulator as a human type agent positioned in her/his

residence (or her/his shop for merchants).

Following the bio-inspired approach described in

Le Fur et al. (2023), each inhabitant is characterised

by attributes unique to either agents (name, age,

location, energy), containers (containing container,

containers contained, including pathogens), animals

(speed, sensing, trapped, etc.), or mammals (mating

latency, pregnancy, sexual maturity, suckling child,

gestation length, etc.). The value of each attribute is

provided either from data collected in the field (sex,

age, pregnancy status, etc.) or from literature (max

age, speed, sensing, etc.).

2.3.3 Rodent Trapping

Secondly, a new ten days survey was carried out in

the city to sample the main animal actors involved in

the epidemic risk. Traps were placed in selected

rooms several nights in a row and the rodents present

were captured. Following a standardised protocol,

each mouse capture led to biometric measurements,

observation of the genitals and an autopsy with

samples taken from different organs. In the model

each rodent observation was implemented as it has

been for humans by characterizing each agent by its

class attributes, acquired in the field (sex, estimated

age, pregnancy status in the case of females,...) or

recovered from the literature.

The behaviour of house mice, like that of other

mobile organisms in the model except humans (see

below) is driven by a desire - perception - deliberation

- decision - action type system (Le Fur et al., 2023).

concerning mice, modelling has also been the subject

of specific treatment to account for their particular

behaviour which compel them to move along walls

during their foraging activity. An algorithm has been

developed specifically for this purpose (Sall et al.,

2019).

2.3.4 Ticks

The protocol for collecting ticks involved locating

rodent burrows and crevices inside homes and in the

courtyards of compounds visited for rodent sampling,

and then vacuuming the contents of these holes with

a thermal vacuum cleaner. Each sample was then

visually inspected for ticks, and the collected ticks

were then placed in tubes.

The identified ticks were reified in the model

using an object class inheriting from the animal

category (sensing, velocity, etc.) with tick-specific

properties. In this case, additional attributes have

been added to represent the different stasis through

which these animals pass (egg, larvae, nymph, and

adult) as well as the processes of deliberation -

decision (waiting in a burrow, climbing on a rodent

or a human) and the timings allowing them to switch

from one to the other by incorporating three specific

attributes, namely the duration of a meal, the duration

of hibernation after a meal, and the number of meals

(McCoy and Boulanger, 2015).

2.3.5 Cats

During the survey to trap rodents and sample ticks,

the presence of cats was estimated based on direct

observations coupled with a questionnaire

administered to inhabitants. As precedently, the

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

136

identified cats were reified into a distinct class

inheriting the traits of mobile mammals completed

with a relatively simple specific behaviour (sleep for

14 hours during the day, one meal at home per day

and a search activity for rodents in the streets of the

village at night).

2.3.6 Bacteria

Finally, the rodent and tick samples were sent to a

laboratory for bacterial strain identification (Ouarti et

al., 2022). When infected rodents or ticks were

identified, bacteria-like agents were reified and

introduced into the mouse or tick agents, respectively,

within the simulator. There are no agent-specific

attribute or behaviour for bacteria which are passively

transmitted from inside one agent to inside another

during a tick bite in the current model (mouse to tick,

tick to mouse, tick to human).

2.3.7 Human Activity

After completing this initial inventory of the present

protagonists, a decisive new field survey was

conducted to comprehend the daily rhythm of the city.

The purpose then was to complete the model in order

to determine and quantify the human activity periods

that are susceptible to epidemic risk (house mouse

movements, tick bites). Over the course of eight days,

every house in the city centre was re-visited, and the

residents (adults and adolescents) were questioned

individually about their daily activities, hour by hour,

as well as the time and place where they slept. These

surveys resulted in the recording of 3,519 instances of

activity involving 489 inhabitants and hence enabling

a nearly exhaustive description of daily human

activity in the city center. The wide variety of activity

obtained was synthesized following a logic of

appreciation of the possible interaction between

humans on the one hand and rodents or ticks that can

transmit diseases on the other. Each of these activities

has thus been categorised into four main types: (i)

movement corresponding to daily activity including

travel, (ii) wakefulness corresponding to the times

when the inhabitants remain steady but with their

eyes open and can perceive their environment

(prayer, internet, etc.); (iii) rest which is associated

with sleep (night, siesta) and during which the

inhabitants do not perceive their environment, finally

(iv) meals during which food can constitute an

olfactory stimulation and behavioural changes for

rodents. the 3,519 activity instances were thus

compiled and transcribed in this way before being

added to the simulator's database.

Given the explicit nature of the activity sequences

obtained as a result of this work, the human agents in

the model were not subjected to a process of

deliberation for their actions, as was the case for mice,

cats, and ticks; instead, the activity sequences

obtained for each resident were used to determine

their actions. Regarding movement, activities that

require moving through city streets were coded using

the A-star algorithm (Tjiharjadi et al., 2022), which is

a path finding algorithm in a graph that finds the

costless route from a starting point to a destination. It

was developed here, using an existing code (Suriabe,

2017), to formalise the population movements in the

graph of the city's streets (i.e., without crossing walls

and houses).

3 RESULTS

3.1 Cartography

A detail of the cartography that was obtained and

which serves as support for the simulator is presented

in Figure 1. The entirety of the domain (Figure 2)

represents the urban core as a grid of 586 x 599

(351,014) cells arranged into 1,326 units or landplots

(room, wall, door, shop, fence, yard, corridor, road,

etc.). When food stocks have been identified during

the census, they are reified and placed in the simulator

as objects.

Figure 1: Detail (31x24 cells, 0.21% of the domain) of

downtown Dodel and the digitization of habitations (one

cell = 1 m

A Digital Twin Simulator Approach as a Support to Develop an Integrated Observatory of the Epidemic Risk in a Rural Community in

Senegal

137

Figure 2: Diel activity declared replicated in the simulator. The snapshot displays the entire domain being modelled.

A 24h animation is available at https://youtu.be/LXfPxZbK-74.

3.2 Human Activity

Nonetheless, it is mainly with the exhaustive census

of human activities transcribed in the simulator that it

was possible to obtain the digital twin that mimics

diel activity in the city. Indeed, by combining detailed

cartography with a faithful representation of human

activity, we obtain the dynamic environment that

organisms (ticks, mice, cats) must account for in their

own activity.

Figure 2 for instance depicts a snapshot of the

early morning activity recorded in the studied area. At

that time, children leave for secular school in the

south of the village (a), while others are already

present at the Koranic school (b); the central market

begins to bustle (c) with vendors already present in

the shops surrounding the central market (d).

Using this representation, it is possible to simulate

an environment that is close to reality, with periods of

calm (meals, naps, rest) interspersed with areas that

are occasionally active (school, mosque, central

market, etc.). As animal agents (primarily mice)

evolve in response to human activity, the patterns of

activity resulting from their interaction (flee, hide)

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

138

with human presence appear realistic; for instance,

there are significantly more mouse movements in the

village during the middle of the night, even if

movements of mice can still occur occasionally

during the day, which is indeed observed. These

movements are therefore not programmed here but

generated by human activity.

3.3 Integrated Simulation

Field observations related to the presence of ticks,

cats, house mice were analysed and reported on a

static data portal (http://simmasto.org/infos/052).

Observed individuals were coded in the simulator.

Within one standard scenario, the simulated system is

composed of 489 humans, 135 cats, 175 mice, 68

ticks.

The simulator then makes it possible to

specifically examine the various configurations

obtained within the dwellings and the resulting

interactions between agents within rooms. For

instance (Figure 3), it is possible to study how

inhabitants move from the courtyard to their room

according to the time of day, how house mice hide

when human activity falls into the categories "meal",

“movement” and “awake” and how they are free to

roam outside their nests when humans are distant or

in the “rest” category.

Figure 3: Detail (44x53 cells or m

, 0.66% of the domain)

of a dwelling including a courtyard and rooms where people

live, as well as food-selling shops on the side of the road,

and in this instance a pregnant female and an adult tick (data

from observation and transcribed in the simulator).

3.4 Detailed Processes Simulation

Finally, the interactions between animals themselves,

such as the reproductive behaviour of house mice

(Figure 4) or the conditions under which ticks can

infect or become infected from mice, can also be

studied in detail (Figure 5).

Figure 4: Study of behaviours and interactions between

house mice within rooms (agents’ label: agent id / target or

desire in the absence of target): in this sequence, a male

(4439) and a female (4436) mouse walk close to a room

walls, the male then perceives the female, identifies it as its

target and moves towards it. In the following sequence,

reproduction can occur, and then the male and pregnant

female resume their independent foraging activity.

Figure 5: Study of behaviours related to the interaction

between vectors and reservoirs. In the example illustrated,

the modelled room contains a mouse nest (704007) where

ticks seek to feed on the mice present. Tick 704084 remains

attached to mouse 13284 as it leaves the nest to feed, while

tick 704082 remains in the nest.

A Digital Twin Simulator Approach as a Support to Develop an Integrated Observatory of the Epidemic Risk in a Rural Community in

Senegal

139

Figure 6: the three benefits of placing a digital twin simulator at the core of a long-term system for monitoring epidemic risk

in an urban setting.

4 DISCUSSION

As part of the development of a multidisciplinary

epidemic risk observatory, we have incrementally

developed a digital twin of the Senegalese village of

Dodel. In the context of successive stages, we

conducted round trips between the field and digital

coding to primarily (i) develop a high-resolution map

of all homes and shared places in the city centre, (ii)

introduce the census of all inhabitants, (iii) sample

and formalise the presence of agents such as house

mice, cats, ticks and bacteria and finally (iv) identify

and code the details of the daily activity of all

inhabitants. The simulator obtained is hence capable

of representing, both globally at the level of the city

centre and locally at the level of rooms, most of the

interactions that can occur in this rural municipality.

The simulator was positioned at the core of the

observatory's operation. At the conclusion of the

process, this approach proved fruitful by contributing

three useful and complementary functionalities

(Figure 6). Following the initial approach, the

simulator constitutes a dynamic and integrated

database of knowledge gathered by all involved

disciplines. By providing agents with behaviours, not

only can the entire system be represented in a unified

manner, but it can also be brought into interaction.

On the other hand, as the simulator is based on a bio-

inspired approach, it is potentially robust to any

improvement even not anticipated during the model's

initial design. For instance, in recent surveys,

additional data were collected at the health post level

on humans with fever, including information about

the nature of the fever. Due to the bio-inspired

approach used to develop the digital twin, such

features can be added and incorporated into the

simulator without calling the model into question.

1. Digital twins have been recognized as platforms

for consensus building among stakeholders

(Okita et al., 2019) which may be the case here

for the simulator produced. Thanks to the high

level of realism provided by the model following

the digital twin approach, the dynamics

generated are straightforward, easily

comprehended, and communicable (movement

of people, behaviour of mice in rooms and shops,

etc.). In a logic known as companion modelling

(Barreteau et al., 2003), the population and local

(mayors, village chiefs) and regional

(department) decision-makers can evaluate or

criticize the results and help propose operational

adjustments to the observatory in response.

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

140

2. As the simulator is based on a data-driven logic,

it may exhibit issues during simulations that can

be analysed to reveal areas or spaces where the

field effort and the collected data are either

insufficient or excessive. Within the framework

of a constant back-and-forth between the field

and the simulator's representation of the results,

the tool constituted by the simulator is therefore

a very useful instrument for guiding the field

survey protocols.

4.1 Calibration Issues

The resulting model incorporates a large number of

distinct actors, each with their own distinct

deliberation and action behaviours. The urban

environment presents additional challenges because

agents must move in a variety of ways: along walls,

in a network of streets, or linearly in rooms; avoiding

numerous obstacles; failing to perceive nearby

objects because walls obscure them, etc. On the other

hand, field-based datasets are abundant, particularly

those pertaining to human activity. This complexity

present challenges for assembly calibration, which

can be linked to (i) data coding (e.g., a person

sleeping in an awkward position in the middle of the

street) or (ii) modelled behaviour (e.g., a hungry cat

eats another cat). In this second category, the object-

oriented and bio-inspired approaches used to develop

the model allow for the resolution of the vast majority

of inconsistencies, but leave room for a few

incoherent behaviours. The identification of aberrant

behaviours is more often than not based on the

observation of simulations that are then corrected.

This is a lengthy process, and the calibration of the

model in this study is still ongoing but it does not

constitute an insurmountable obstacle.

5 CONCLUSION

The process of creating a digital twin of a complex

urban environment is lengthy and delicate.

Nonetheless, once the computer system has

stabilised, one can get a tool with decisive

advantages. The first is to benefit from a dynamic

restitution of knowledge that goes beyond what a

sophisticated multidisciplinary DBMS could provide.

The second is the digital twin's utility as a means of

communication. As an anecdote, during the last

restitution to the village authorities, the interest

generated by the transmission of the results prompted

the village chief to broadcast a message via the

mosque asking for a good reception of scientists by

the population as well as a request to the project team

to make a public restitution of the work conducted to

the population.

This work hence suggests that the digital twin

paradigm can be adapted and applied to complex

social issues such as the management of an

observatory for the monitoring of epidemic risk

involving multiple human-animal actors evolving in

a variety of environments. This approach may

therefore be proposed as an efficient mean to meet the

methodological requirements raised by the EcoHealth

approach.

ACKOWLEDGEMENTS

The study was supported by the French National

Research Institute for Sustainable Development

(IRD), the ‘Centre de Biologie pour la Gestion des

Populations’ (CBGP, UMR no.22 INRAe / IRD /

Cirad / Supagro), the Cerise (grant IRD-FRB

no.AAP-SCEN -20B III) and CEA-MITIC (Univ.

G.Berger, Saint-Louis Sénégal) projects as well as the

ObsMice (West African Small Mammal Observatory

Indicators of Environmental Change) network. We

also wish to thank Y.Niang, M.Kane, N.Sarr, O.Sall,

R.Dia, G.Diatta for their decisive field support and

O.Sall for detailed mapping of the Dodel town.

Finally, we would like to warmly thank the

population and administration of Dodel for their

hospitality and willingness to answer questions

during our numerous surveys.

REFERENCES

Adrion, E.R., Aucott, J., Lemke, K.W., Weiner, J.P. (2015).

Health care costs, utilization and patterns of care

following Lyme disease. PloS One 10(2): e0116767

Auffray, Y., Barbillon, P., Marin, J.-M. (2011). Modèles

réduits à partir d’expériences numériques. J. Société Fr.

Stat. 152(1): 89–102.

Barreteau O., Antona M., D'Aquino P., Aubert S., Boissau

S., Bousquet F., Daré W., Etienne M., Le Page C.,

Mathevet R., Trébuil G., Weber J. (2003). Our

companion modelling approach. Journal of Artificial

Societies and Social Simulation, 6 (2):1, n.p.

http://jasss.soc.surrey.ac.uk/6/2/1.html

DeAngelis, D.L., Mooij, W.M. (2003). In praise of

mechanistically rich models. In: Canham, C.D., Cole,

J.J., Lauenroth, W.K.(Eds.) Models in Ecosystem

Science. Princeton Univ. Press, Princeton, New

Jersey:63-82.

Elbir, H., FotsoFotso, A., Diatta, G., Trape, J.F., Arnathau,

C., Renaud, F., Durand, P. (2015). Ubiquitous bacteria

A Digital Twin Simulator Approach as a Support to Develop an Integrated Observatory of the Epidemic Risk in a Rural Community in

Senegal

141

Borrelia crocidurae in Western African ticks

Ornithodoros sonrai. Parasit. Vectors 8(1): 1–5

Grieves, M., Vickers, J. (2017). Digital Twin: Mitigating

Unpredictable, Undesirable Emergent Behavior in

Complex Systems. In: Kahlen, J., Flumerfelt, S., Alves,

A. (eds) Transdisciplinary Perspectives on Complex

Systems. Springer, Cham. doi.org/10.1007/978-3-319-

38756-7_4

Jones, K.E., Patel, N.G., Levy, M.A., Storeygard, A., Balk,

D., Gittleman, J.L., Daszak, P. (2008). Global trends in

emerging infectious diseases. Nature 451(7181): 990–

993.

Le Fur, J., Mboup, P.A., Sall, M. (2017). A simulation

model for integrating multidisciplinary knowledge in

natural sciences. Heuristic and application to wild

rodent studies. In: 7th International Conference on

Simulation and Modeling Methodologies, Technologies

and Applications, pp. 340–347. Scitepress DOI:

10.5220/ 0006441803400347

Le Fur, J., Mboup, P.A., Sall, M. (2023). Growing

Bioinspired Synthetic Landscape Ecologies and the

Adequacy of Object Oriented Programming. In:

Wagner, G., Werner, F., Oren, T., De Rango, F. (eds)

Simulation and Modeling Methodologies, Technologies

and Applications. Lecture Notes in Networks and

Systems, vol 601. Springer, Cham.

https://doi.org/10.1007/978-3-031-23149-0_7

Le Fur, J.; Mboup, P.A., Sall, M. (2021). Use and Adequacy

of Computer Paradigms to Simulate Bioinspired

Synthetic Landscape Ecologies. 11th Internat. Conf.

Simul. and Model. Method., Technol. and Applic., pp.

154-162. Scitepress. DOI: 10.5220/0010601101540162

Lefrançois, T., Malvy, D., Atlani-Duault, L., et al. (2023).

After 2 years of the COVID-19 pandemic, translating

One Health into action is urgent. The Lancet,

401(10378), 789-794. dx.doi.org/10.1016/S0140-

6736(22)01840-2

Lisitza, A., Wolbring, G. (2018). EcoHealth and the

Determinants of Health: Perspectives of a Small Subset

of Canadian Academics in the EcoHealth Community.

Int. J. Environ. Res. Public. Health 15(8): 1688.

McCoy, K.D., Boulanger, N. (Eds.) (2015). Tiques et

maladies à tiques : Biologie, écologie évolutive,

épidémiologie. IRD Éditions.

Mencke, N. (2013). Future challenges for parasitology:

vector control and ‘One health’in Europe: the

veterinary medicinal view on CVBDs such as tick

borreliosis, rickettsiosis and canine leishmaniosis. Vet.

Parasitol. 195(3–4): 256–271.

Nie, Q., Tang, D., Liu, C., Wang, L., Song, J. (2023). A

multi-agent and cloud-edge orchestration framework of

digital twin for distributed production control. Robotics

and Computer-Integrated Manufacturing, 82, 102543.

doi.org/10.1016/j.rcim.2023.102543

North, M.J., Collier, N.T., Ozik, J., Tatara, E., Altaweel,

M., Macal, C.M., Bragen, M., Sydelko, P. (2013).

Complex Adaptive Systems Modeling with Repast

Simphony. Complex Adaptive Systems Modeling,

Springer, Heidelberg, doi.org/10.1186/2194-3206-1-3

Okita, T., Kawabata, T., Murayama, H., Nishino, N. Aichi,

M. (2019). A new concept of digital twin of artifact

systems: Synthesizing monitoring/inspections,

physical/numerical models, and social system models.

Procedia CIRP., 79 (2019), pp. 667-672,

dx.doi.org/10.1016/j.procir.2019.02.048

Ouarti, B., Sall, M., Ndiaye, I., Diatta, G., Diarra, A. Z. ,

Berenger, J. M.,Sokhna, C., Granjon, L., Le Fur, J.,

Parola, P. (2022). Pathogen Detection in Ornithodoros

sonrai Ticks and Invasive House Mice Mus musculus

domesticus in Senegal. Microorganisms 2022, 10,

2367. doi.org/10.3390/microorganisms10122367

Rotureau B, Waleckx E, Jamonneau V, et al. (2022).

Enhancing research integration to improve One Health

actions: learning lessons from neglected tropical

diseases experiences. BMJ Global Health

2022;7:e008881. dx.doi.org/10.1136/bmjgh-2022-

008881

Sall, M., Dembélé, J.M., Le Fur, J. (2019). An hybrid

algorithm to simulate mice following residential wall.

Proc. 8th Internat. Conf. Simulation and Modeling

Methodologies, Technologies and Applications

(SimulTech), SCITEPRESS Publ.: 368-375

dx.doi.org/10.5220/0007978303680375

Suriabe, M. (2017). A-Star-Java-Implementation

github.com/marcelo-s/A-Star-Java-Implementation

Tao, F., Xiao, B., Qi, Q., Cheng, J., Ji, P. (2022). Digital

twin modelling. J. Manuf. Syst., 64: 372-389

Tjiharjadi, S., Razali, S., Sulaiman, H.A. (2022). A

Systematic Literature Review of Multi-agent

Pathfinding for Maze Research. J. Adv. Inf. Technol.

Vol 13(4).

White, G., Zink, A., Codecà, L., Siobhàn, C. (2021). A

digital twin smart city for citizen feedback. Cities 110

103064

Zinsstag J., Schelling E., Waltner-Toews D., Tanner M.

(2011). From “one medicine” to “one health” and

systemic approaches to health and well-being. Prev.

Vet. Med. 2011; 101:148–156.

dx.doi.org/10.1016/j.prevetmed.2010.07.003.

SIMULTECH 2023 - 13th International Conference on Simulation and Modeling Methodologies, Technologies and Applications

142